This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle
What I Built
meal-planner builds a week of meals around your life: where you live, the food you grew up with, what's in your cupboard, the pans you own and the money you can spend. Then it gets you to a target weight with food you'd actually cook.
Most diet apps hand everyone the same chicken-and-broccoli plan. It doesn't care that you live in Pune and eat poha for breakfast, that your hostel kitchen is one induction plate, or that your food budget is ₹200 a day. meal-planner starts from the opposite end:
- You type where you live, in any language. "Lyon", "Baramulla", "Москва", "Kraków". It loads what households there actually cook and buy, priced in the local currency (₹, £, €, R$, CLP…).
- You describe a normal day in plain words. No dropdown of foods you don't eat. An agent turns "tea with two sugars, two parathas, rajma-chawal at lunch…" into meals with calorie estimates, flags conflicts ("you said vegetarian but mentioned chicken"), and asks up to three follow-up questions when it matters.
- You list your pantry the same way. A second agent sorts it, and your shopping list only contains what's missing.
- You get a 7-day plan: meal times, portions in local household measures (katori, cups, slices), recipes that only use your equipment, the shopping list, and a chat assistant for swaps ("make Wednesday's dinner cheaper"). Every swap is a proposal you approve with one click.
Who would pay:
- Nutrition coaches and dietitians with clients in different countries. Today they hand-edit the same template for a vegetarian client in Pune and a halal client in Manchester; this does the first draft in seconds.
- Students, expats and people cooking on tight budgets who've been failed by generic plans.
The model behind it runs on free tiers today, so a free tier for individuals and a small monthly plan for coaches are both viable.
Demo
🔗 Live: https://kilobite-planner-web-lv88.onrender.com
(Free Render instance: the first visit after a quiet spell can take ~1 minute to wake up.)
A generated week: meals with times, portions, macros and cost per day; green "added to hit your targets" items from the balancer; the day's totals against its targets; and a shopping list of only what's missing from the pantry.
Code
meal-planner 🍲
Weekly meal plans built around where you live, the food you grew up with, what's in your kitchen and what you can spend on open-weight models only, routed automatically per task.
How it works
- Your body: sliders; calorie and macro targets come from standard formulas (Mifflin-St Jeor, WHO BMI), with safety limits.
- Your food: type any place ("Lyon", "Baramulla", "Москва"). It's matched against a knowledge base of cuisine profiles (staples, reference dishes with nutrition, meal times, currency, local budgets). Unknown places are mapped by a fast model and a new cuisine profile is written by Gemma 4 in the background and saved, so the next lookup is instant.
- Your day: describe a normal day in plain words. A Mastra intake agent grounds it in the knowledge base, estimates calories, flags diet conflicts and asks follow-up questions.
- Your kitchen: equipment, plus a free-text pantry read by a Mastra…
-
backend/: FastAPI. Calorie maths, knowledge graph, model router, planner, balancer and variety checks, tests and evals. -
frontend/: Next.js app plus the Mastra agents (intake, pantry, chat) and the topic guardrail. -
render.yaml: the Render Blueprint for both services.
How I Built It
The rule: models pick the dishes, code does the maths
Small mistakes in calories hurt people, so no model ever decides a number that matters:
- Targets come from formulas: Mifflin-St Jeor energy needs, a calorie floor (1,200/1,500 kcal), weight loss capped at 1% of body weight per week, and no target below BMI 18.5.
- The model drafts the week; code re-adds every day. A deterministic balancer then fixes days that fall short. It adds a diet-safe top-up (tofu for a vegan, cottage cheese for a vegetarian), or swaps out a serving of the rice or bread that day already contains to make room for protein. Every change is marked in green in the UI.
- Variety is checked by code too. If a main dish appears more than twice a week, or two days are identical, only those days go back to the model, with a "don't use these again" list.
Open-weight models, routed by task (no model picker)
| Task | Models (first available wins, the rest are fallbacks) |
|---|---|
| Full week plan | gpt-oss-120b (Groq) → Qwen 3.8 27B → Gemma 4 26B-A4B / 31B |
| Reading location, day and pantry text | Qwen 3.8 27B → gpt-oss-120b → Gemma 4. Non-Latin text goes to Gemma 4 first (trained on 140+ languages). |
| Writing a new region into the knowledge base | Gemma 4 → gpt-oss-120b |
| Chat with tool calls | gpt-oss-120b → Qwen → Gemma 4 |
| Topic guardrail | gpt-oss-safeguard-20b |
Every model is open-weight behind an OpenAI-compatible API, so swapping hosts is a config change.
Making Gemma 4 practical on a free tier:
- Thinking that can't be turned off: Gemma 4 on Google AI Studio always "thinks" before answering, and the API rejects every setting to turn it off. One line in the system prompt ("think briefly, then output compact JSON") cut an outline call from 82 s to 24 s.
- Slow output: at about 40 tokens/s a full week in one call takes minutes. So for Gemma the planner writes a 7-day menu outline first, then fills days and recipes in parallel.
- It works when it counts: during testing, Groq's free quota ran out mid-evaluation and Gemma 4 produced a complete, valid Chennai week (eggetarian, no onion or garlic) that passed every check.
Mastra agents
- Intake and pantry agents use Mastra structured output with per-request model chains. They're grounded in the knowledge base (the same data the Atlas graph holds), which supplies nutrition for the dishes you mention and your region's usual meal times.
-
The chat agent has three tools:
searchFoods(knowledge-base nutrition lookup),getDayandproposeSwap. Swaps come back as cards with an Apply button; the agent can't change your plan by itself. -
A custom input processor runs
gpt-oss-safeguard-20bagainst a written policy. Anything that isn't about food, diet or your plan is refused in about a second, before it reaches the main model.
A knowledge graph on MongoDB Atlas
Regions, cuisines, dishes and diet rules live in Atlas as a graph:
place ─IN→ country ─USES→ currency place ─EATS→ cuisine ─HAS_DISH→ dish ─CONTAINS→ tag
diet / restriction ─EXCLUDES→ tag allergy ─IS→ tag tag ─INCLUDES→ tag
-
Diet rules are a hierarchy. "Vegan" excludes
animal_product, and a$graphLookupwalks animal_product → meat → red_meat → beef. "Which French dishes can a halal pescatarian with a peanut allergy eat?" is one traversal. - The graph grows itself. 14 cuisines are hand-curated. Any other place is mapped by a fast model in under a second, then Gemma 4 writes a full cuisine profile in the background (Polish took 69 s) and saves it to Atlas, so the next person searching Kraków gets it instantly, even after a redeploy.
- Atlas is reached with X.509 certificate authentication. Without it, everything falls back to a local JSON copy with identical rules, and the tests check the two agree exactly.
The knowledge graph runs on a free Atlas cluster (AWS Mumbai, 3-node replica set).
Seeing what the agents do: Sentry
-
Backend: every plan is an
invoke_agent plannertrace, with each model call (model, latency, tokens) and the draft, variety and balance steps nested inside. - Agents: Mastra's Sentry exporter sends traces for the intake, pantry and chat agents and their tool calls.
- Privacy: prompt and answer text is redacted before it leaves the server. Traces show the shape of a run, never what a user typed.
Sentry's AI Agents dashboard for a test session: agent runs, estimated cost, duration, and calls and tokens per model. gpt-oss-120b made 6 calls (20,403 tokens), Qwen 3.8 27B 3 calls (1,247 tokens), and the gpt-oss-safeguard-20b guardrail 1 call (454 tokens), for a total estimated cost of under one US cent.
Tested against real-world data
- Nutrition vs USDA: I pulled USDA FoodData Central's survey foods and compared 27 knowledge-base dishes per portion. The median gap was 29%. Fixing the 11 worst dishes (my palak paneer and cappuccino were far too high) brought it to 17%, and the test now fails if any dish drifts more than 60%.
- Currencies: checked against an open countries dataset. 12/12 places outside the knowledge base (São Paulo, Lagos, Seoul, Hanoi…) resolved to the right currency.
-
End-to-end evaluation: 10 region and diet profiles (Pune vegetarian, Baramulla halal, London on a tight budget, Lyon vegetarian with a peanut allergy, Moscow pescatarian, Mexico City gluten-free, Tokyo high-protein, New York vegan, Kraków keto, Chennai eggetarian with no onion or garlic). Across the 7-day plans:
- Calories on target on 70/70 days, and the correct currency for every region
- No real diet violations. The keyword scanner flagged 3 meals, all false alarms (goat's cheese and tofu yogurt).
- 17–27 distinct dishes per week, after the variety fix took Lyon from 9 to 26
- 97 offline tests cover the maths, rules, graph-vs-local agreement, balancer and variety. Live tests call every model directly, so a retired model or broken key shows up immediately.
Deployment
Two Render services from one Blueprint: FastAPI, and Next.js with the Mastra agents. The browser reads the API address at runtime, so moving the backend never needs a rebuild.
Both services live on Render's free tier in Singapore: the Next.js web app with the agents, and the Python API.
What went wrong (and what it taught me)
- Llama 3.3 was retired from Groq mid-build. The router made switching to gpt-oss-120b a one-line change.
- Telling Gemma to "adjust portions until within 5%" made it re-add numbers in its head for over 3 minutes and never answer. Telling it the truth ("our code re-checks the totals") fixed it. Models are bad at arithmetic, so stop asking them to do it.
- My first plans rotated the same 9 dishes all week. The cause was my own prompt ("reuse dishes, at most 10 recipes"). Variety is now a measured, enforced check.
-
Groq's free tier rejects a request when prompt plus output allowance exceeds its per-minute budget. A longer prompt caused
413errors until I capped the output allowance and recipe count. - Protein for plant-based diets at low calories is the hardest target. Even with the balancer, vegan and vegetarian plans sometimes land 10% under on a couple of days. The app says so instead of hiding it.
Why Does Open Innovation Matter?
- The business only works because the models are open and cheap to run. A week of meals is a long structured answer. On open models served on free and low-cost tiers it costs nothing today, which is what makes a free tier for individuals and a cheap coach plan realistic. With per-token prices from a closed API, that margin is gone.
- No lock-in. I swapped models mid-build without touching product code. Llama 3.3 was retired and gpt-oss took its place; Gemma 4 covers for gpt-oss when Groq is rate-limited. Every model speaks the same API, so the router is just an ordered list per task.
- Different open models for different jobs. gpt-oss-120b for long plans, fast Qwen for extraction, Gemma 4 for multilingual input and world-food knowledge, gpt-oss-safeguard for a written policy guardrail. One closed model would have to do everything at one price.
- Your health data doesn't have to leave you. Answers and plans live only in your browser, and nothing about a user is stored server-side. The same open models also run locally through Ollama: an offline mode routes planning to a local Gemma model, though I haven't tested it end to end yet.
- I can inspect and fix it. Because the stack is open (Mastra, the models, the data), I could measure exactly where it was wrong against USDA data and fix the knowledge, rather than hoping a black box gets it right.
My Agent Session
The whole build, with Claude Code: from "diet apps ignore what's in your kitchen" to a deployed multi-agent app. It includes every wrong turn: a model retired mid-build, Gemma thinking itself into a 3-minute loop, the same 9 dishes on repeat, and the Render deploy debugging. API keys, connection strings and private paths were scrubbed before saving.
im taking part in oktoberfest and for week 2, the challenge prompt is this -
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Side Hustle
Build something with open-source AI at its core.
That can mean running an open-weight model, building on an open-source agent harness or framework, running inference locally, or all three. Whatever you pick, the open pieces should be what makes your project work.
In your post, tell us why open innovation matters for what you built. Does it run on a laptop with no internet? Keep someone's data off a server they don't control? Let you fine-tune, swap models, or change how your agent behaves? Cost nothing to run? Tell us where your open-based approach worked better than a closed one.
This Week's Theme: Side Hustle
Build something with open-weight models or open-source AI that someone would actually pay for.
That can mean running an open-weight model, building on an open-source agent harness or framework, running inference locally, or all three. Whatever you pick, the open pieces should be what makes your project work.
Think small, specific, and useful. The best builds here solve a real problem for a real person, whether that's a freelancer, a small business owner, a niche hobby community, or your friend who keeps complaining about the same tedious task. A few ideas to get you going:
An invoice and receipt sorter for freelancers that runs locally, so client data never leaves their laptop
A listing writer for Etsy or eBay sellers that turns a few product photos into a polished description
A menu translator for a local restaurant that handles their specific dishes better than a generic tool
In your post, tell us why open innovation matters for what you built. Does running your own model keep costs low enough that the business actually works? Keep your customers' data private? Let you fine-tune for a niche that a general-purpose API handles poorly? Avoid getting locked into someone else's pricing? Tell us where your open-based approach worked better than a closed one.
Bonus points if you land your first user. Get it in front of someone, let them use it, and tell us how it went. Did they pay? Would they? What did they ask for next?
Submission Template
Show your work. We'd love to see how you built it. Save your agent session with DevRelay and embed it in your post, or link to it. It's optional, but it helps the judges understand your process.
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what i have been thinking of is creating a project for fun recipies. most of the diet plan apps available right now in market are mostly like they give you recommendations on what data is generic to the models. it doesnt care if you have the ingredients available, the infra available or even if you have the money to make the stuff. lets make an app where you input your daily food habits, what you would eat in a week, what your diet habits are, what region you are in, and then give you recommendations on how much food to eat, what to eat when, and the recipes to reach your target weight.
I checked your setup. The project folder is empty and Ollama is installed, but you haven't pulled any models yet. The command is stuck on the Python, Node and GPU checks, so I don't know your hardware or runtimes yet.
What I think of the idea
It's a good fit for the theme. The pitch is clear: diet apps hand out generic plans, and this one plans around what's in your kitchen, what equipment you have, your budget and your local cuisine. That's a problem real people complain about.
To make it work well for the judges:
- Do the calorie and macro math in plain code, not the model. Use a standard formula (Mifflin-St Jeor) to work out how much energy you burn and a calorie target from there. Add hard safety limits: a calorie floor, a cap of about 0.5–1 kg loss per week, and a "this isn't medical advice" note. Small open models get arithmetic wrong, and a wrong calorie number is the one mistake that actually matters here.
- Use the open model for what it's good at. It turns "I usually eat poha, dal-rice, chai with 2 sugars" into structured data, and it writes recipes using only your pantry, equipment (say, a pressure cooker and one burner, no oven), budget and regional dishes.
-
The open-source angle for your post:
- Food habits and weight are sensitive health data, so running locally on Ollama means none of it leaves the laptop.
- It costs nothing per plan, so a cheap or free tier still makes money.
- You can swap or fine-tune the model for regional cuisine that big hosted models handle generically.
- Who pays: a narrow target is more convincing than "everyone." Two options: students or hostel cooks with a hot plate and ₹150 a day, or a small nutritionist or gym coach who wants to give clients localized plans without sending their data to a cloud API. The coach is the stronger "someone would pay" story, and that coach could also be your first user.
The build I'd suggest (fits in a week)
- Model: Qwen3 8B or Llama 3.1 8B on Ollama, with JSON-schema output so the plans come back in a fixed structure.
- App: a small Python FastAPI backend with a simple web UI. Streamlit would be faster to build if you'd rather.
-
Flow:
- You enter your profile: age, height, weight, target weight, activity level, region, budget, equipment, pantry, and your usual week of food in plain text.
- The code calculates your calorie target.
- The model plans a week of meals with timings and portions.
- The code checks the plan against your targets and asks the model to redo any days that miss.
- You get the recipes and a shopping list of only the items you're missing.
Three questions before I start building:
- Stack: is Python + FastAPI + plain HTML/JS okay, or would you prefer Streamlit or a JS framework?
- Hardware: do you have an NVIDIA GPU, and how much RAM? That decides between an 8B model and something smaller like a 3–4B one.
- Region: should the first version focus on Indian food and rupee budgets, so we can make it really good at one region and keep it swappable?
Once you answer, I'll set up the project and pull the model.
not in plain text, but like a very interactive form. the usuals like height, weight, age, target weight, activity level will be page one of the form, then come in more details in the next page like your region, ethnicity (as it can also decide your usual eating habits), what you eat in a day usually, type of eater (veg, non-veg, eggeterian, pesciterian, halal, kosher, and rest of the generic things), and these should be also interactive.
for building i would go with python + fastapi + vercel/nextjs for UI, and lets start with building around the indian region too. the only issue is with hardware, as i have 6GB + 2 GB integrated Intel Graphics card. so definitely these models would not run on them. what other options do i have then (which are ofcourse free first priority and then if not lets compare the costs) or can it run on my local as well?
I checked your machine, and it's a bit different from what you described. You have 6 GB of RAM and no dedicated GPU. The CPU is a Ryzen 5 3450U, and the 2 GB of graphics memory is AMD Vega 8 integrated graphics borrowing from that same 6 GB, not Intel. So your hardware question really comes down to whether a model can run on the CPU in 6 GB.
Can it run locally?
Yes, but only small models, and slowly. Windows, a browser and the Next.js dev server will already use about 3–4 GB, leaving roughly 2–3 GB for the model.
| Model (on Ollama) | RAM needed | Rough speed on your CPU | Usable? |
|---|---|---|---|
qwen3:1.7b |
~1.5 GB | ~8–12 words/sec | Yes; fine for turning form answers into data and short recipes |
gemma3:4b / qwen3:4b
|
~3 GB | ~3–5 words/sec | Barely; a full week's plan takes several minutes |
| Any 8B or larger | 5+ GB | — | No |
The speeds are my estimates, not measurements. Running locally is good for an offline "private mode" demo, but too slow to be the main experience.
Free options for open-weight models
These services host open-weight models and speak the same API that Ollama does. So we write the code once and switch between local and hosted by changing a single setting:
| Option | Models | Free tier | Notes |
|---|---|---|---|
| Groq | Llama 3.3 70B, Qwen3 32B, gpt-oss | Free, with rate limits | Very fast. My pick for the main engine. |
| Cerebras | Llama, Qwen, gpt-oss | Free, with daily token limits | Also very fast; a good backup |
OpenRouter :free models |
Llama, Qwen, DeepSeek, Gemma | Free, about 50 requests a day | Many models to choose from; low limits |
| Kaggle / Colab | Anything up to ~14B, on a free T4 GPU | ~30 GPU hours a week on Kaggle | Not for serving the app. Good for fine-tuning later, e.g. on Indian recipes |
If you outgrow the free tiers
Hosted open models cost roughly $0.05–0.60 per million tokens. One weekly plan uses about 5–8k tokens, so each plan costs well under ₹1. A closed model like GPT or Claude would cost several times that. I'm quoting these prices from memory and they change often, so check them before you put numbers in your post.
My recommendation
- Default: Llama 3.3 70B or Qwen3 32B on Groq's free tier. You get quality plans in a few seconds and it costs nothing.
-
Private mode: a toggle that runs
qwen3:1.7blocally through Ollama, so no data leaves the laptop. This is the open-source story for your post: the same open models run locally or hosted, you can switch providers without being locked in, and you can fine-tune later on Kaggle. - Deployment: the Next.js frontend on Vercel, and the FastAPI backend on Render's free tier or as Vercel Python functions.
The form
Agreed, it'll be a multi-step form with interactive controls rather than text boxes:
- Step 1, body basics: sliders and steppers for age, height (cm or ft/in), weight and target weight; activity level as illustrated cards; and a live display of your calorie needs, timeline and safe weekly rate as you change things.
- Step 2, food background: a state/region picker (North, South, East, West, North-East, with states under each); community or ethnicity as an optional chip select; and diet type as chips (veg, non-veg, eggetarian, pescatarian, vegan, Jain, halal, kosher, sattvic), plus allergies.
- Step 3, a typical day: a timeline of meal slots (morning chai, breakfast, lunch, evening snack, dinner). Each slot gets a searchable picker of Indian dishes with a portion size, for example "2 rotis + 1 katori dal".
- Step 4, kitchen and budget: equipment toggles (pressure cooker, gas, induction, microwave, no oven), a daily budget slider in rupees, a pantry checklist, and how much time you have to cook.
- Result: a weekly plan with meal times and portions, recipes, and a shopping list of only the items you're missing.
To get started, you just need a free Groq API key from console.groq.com; I can't create that for you. Meanwhile I can set up the project: a backend/ folder for FastAPI with a switch between Groq and Ollama, plus the calorie math and safety limits, and a frontend/ folder for Next.js with the multi-step form. I'll also pull qwen3:1.7b for private mode. Shall I go ahead?
going ahead with groq, start with building the UI and backend for the application, the api key is in .env file. this file is part of the .gitignore and never commit this file and hardcode the key value in the code, or put it in any reasoning or in the commit messages or in the actual code
There's no .gitignore yet, so I'll create one first. The key variable is named API_KEY, and I'll only ever load it from the environment at runtime.
While the installs run, I'll write the backend: config, calorie math, an Indian food catalog, the LLM client and the planner.
create-next-app set up its own git repo inside frontend/. I'll remove it so you can make one repo at the project root later.
Next, the catalog: Indian dishes with approximate calories per portion, plus regions, diet types and pantry items. The form and the model's prompt will both use this one list.
Now a quick test of the backend: check the calorie math directly, then start the server.
The calorie math and safety limits work, and the key loads from .env. Now I'll start the API.
Groq returned a 404, which probably means that model has been retired. I'll ask Groq which models are available now.
The server didn't reload after the change. I'll check its log.
The reload hung, which happens on Windows. I'll restart the server.
An old server process is still holding port 8000. I'll stop every process using this project's venv.
Those are the leftover reloader (9080) and its worker (18664) from the server I started earlier, so they're safe to stop.
The backend is done. Now the frontend: I'll look at the scaffolded files.
I'll build the frontend in this order: shared types and API helpers, a theme, UI primitives, the four form steps, the wizard, and the plan view.
Now the theme, layout and proxy config.
I just wrote a circular CSS variable (--font-display refers to itself), so I'll rename the font variables.
Now the reusable UI pieces.
Step 1: body basics with the live numbers panel.
Step 2: food background.
Step 3: a typical day, with a dish search box and a live calorie estimate.
Step 4: kitchen, pantry and budget.
Now the plan view: day tabs, the meal timeline, recipes and the shopping list.
Last piece: the wizard that ties it together (progress, validation, saving your answers between visits, the loading screen).
The header says "your data stays in your browser", but in Groq mode the answers go to Groq. I'll make that claim accurate, then run type-checking and linting.
Type-checking and linting pass. Next I'll restart the backend to pick up the protein change, then start the frontend.
Now an end-to-end test through the Next.js proxy: the page, the catalog, live targets, and a full plan with the new protein check.
The repair pass failed silently. I'll check the server log for the reason.
Nothing was logged, so I'll add logging to the repair step and run the planner directly to see the error.
The TypeScript is fine. Now I'll test the plan directly against the backend, sending the browser's Origin header so CORS gets checked too.
Qwen works in JSON mode, though its first answer called masala dosa a North Indian breakfast. Now I'll build the fallback chain.
Now I'll update the planner to use the new return value and report which model actually wrote the plan.
I'll restart the backend and run the plan again.
No browser tool is available here, so I can't check the UI visually myself. I'll restart the backend, run one last plan with the protein prompt, and write the README.
Now I'll wire the balancer into the planner in place of the LLM repair, and add the per-day total to the output format.
Before spending more tokens, I'll test the balancer offline on the plan that came in 20% under.
Every day now lands within range, and the top-ups respect the diet: a vegan with a gluten allergy gets sprouts and rice, never curd or roti. Next, the frontend needs to show top-ups clearly.
The regex came through correctly. Now I'll restart the backend for one last live run.
Finally, a secrets check. I'll scan every project file (outside .env, node_modules and the venv) for the key's value, without printing it.
The backend and the 4-step UI are built and both running. Open http://localhost:3000 to try it. I couldn't check the UI visually because there's no browser tool here. It compiles and passes type-checking and linting, but please click through it once.
Last live test: a Maharashtra eggetarian with ₹180 a day got a 7-day plan in 11 seconds. All 7 days were within the calorie target, protein and budget: 1,940–2,015 kcal against 1,880, mostly 95–100 g protein against 96, and ₹145–155 a day.
What's built
-
Backend (FastAPI) in
backend/app/:-
nutrition.py: calorie and protein targets worked out in code, with safety limits (calorie floor, max 1% body weight lost per week, no target below BMI 18.5, Indian BMI cut-offs). -
catalog.py: about 100 Indian dishes with calories per portion, regions and states, diet rules, and pantry lists. -
planner.py: builds the prompt and sends it to the model. -
balance.py: adds up every day itself and, if a day falls short, adds diet-safe top-ups like curd or an extra roti.
-
-
Frontend (Next.js + Tailwind), four interactive steps:
- Sliders for age, height, weight and target, activity tiles, and a live panel showing calories, macros, a BMI scale and your goal date.
- Region and state, food culture chips, diet-type tiles, religious rules and allergies.
- A meal timeline with dish search, portion buttons, quick-add chips, and a running "you eat about X kcal vs your target".
- Equipment tiles, budget and cooking-time sliders, and a pantry checklist.
The result page has day tabs, meal cards, recipe pop-ups, a shopping list with checkboxes, and tips. Answers are saved in the browser.
Things that changed along the way
- Llama 3.3 is no longer on Groq. The main model is now gpt-oss-120b, OpenAI's open-weight model under the Apache 2.0 licence. When Groq's free tier rate-limits it, the backend switches to Qwen 3.8 27B automatically, which happened during testing.
- The free tier allows roughly one plan per minute. If both models are limited, users see a "try again in ~Xs" message.
- I dropped the "ask the model to fix bad days" step. It never reliably fixed the numbers and doubled the token use. Plain code now adds the top-ups, and they're marked in the UI. That makes a good point for your post: the open model picks the dishes, and code checks its arithmetic.
- I lowered the protein target to 1.3 g per kg of body weight. 1.6 g/kg was out of reach on an Indian vegetarian or eggetarian budget.
- The browser calls the backend directly, because the Next.js proxy gave up after 30 seconds on longer plans. Deployment settings are in the README.
Secrets
- I created
.gitignorewith.envin it (there wasn't one before). -
.env.examplelists the variable names only. - I scanned every project file for the key's value and found it nowhere outside
.env. The code only reads the key from the environment. - There's no git repo at the project root yet, and I haven't committed anything. I also removed the git repo
create-next-appmade insidefrontend/.
Next steps: click through the form, and if you can, show it to one real person. That's the "first user" bonus. After that we can work on deploying it or on offline mode with Ollama.
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<output-file>~\AppData\Local\Temp\claude\C--Users-bhat--OneDrive-Documents-Karan-Dev-oktoberfest2026-week2\a5896de6-5c0f-4f40-bb97-dbdaa37a8c1f\tasks\b92rmztn5.output</output-file>
<status>killed</status>
<summary>Background command "Check project folder, Ollama, Python, Node, and GPU" was stopped because the system is running low on memory</summary>
<note>This is not a failure of the command. Claude Code stopped it because the system was critically low on memory while the session was idle, which says nothing about the command or its own memory use, so there is nothing in it to debug. Do not start it again on your own, even if the work seems to need it: memory may still be short. Report what was stopped and why, and start it again only when asked. The user can turn this behavior off by starting Claude Code with CLAUDE_CODE_DISABLE_BG_SHELL_PRESSURE_REAP=1 in its environment; setting it from a shell command has no effect.</note>
</task-notification>
<task-notification>
<task-id>bzf21kl3f</task-id>
<tool-use-id>toolu_01MDkvf9LtsFj2j7iEqqFkAc</tool-use-id>
<output-file>~\AppData\Local\Temp\claude\C--Users-bhat--OneDrive-Documents-Karan-Dev-oktoberfest2026-week2\a5896de6-5c0f-4f40-bb97-dbdaa37a8c1f\tasks\bzf21kl3f.output</output-file>
<status>killed</status>
<summary>Background command "Start Next.js dev server on port 3000" was stopped because the system is running low on memory</summary>
<note>This is not a failure of the command. Claude Code stopped it because the system was critically low on memory while the session was idle, which says nothing about the command or its own memory use, so there is nothing in it to debug. Do not start it again on your own, even if the work seems to need it: memory may still be short. Report what was stopped and why, and start it again only when asked. The user can turn this behavior off by starting Claude Code with CLAUDE_CODE_DISABLE_BG_SHELL_PRESSURE_REAP=1 in its environment; setting it from a shell command has no effect.</note>
</task-notification>
<task-notification>
<task-id>bl0rhe6au</task-id>
<tool-use-id>toolu_01U1LZmuikWn2xpDGFYsDAe9</tool-use-id>
<output-file>~\AppData\Local\Temp\claude\C--Users-bhat--OneDrive-Documents-Karan-Dev-oktoberfest2026-week2\a5896de6-5c0f-4f40-bb97-dbdaa37a8c1f\tasks\bl0rhe6au.output</output-file>
<status>killed</status>
<summary>Background command "Start FastAPI server on port 8000" was stopped because the system is running low on memory</summary>
<note>This is not a failure of the command. Claude Code stopped it because the system was critically low on memory while the session was idle, which says nothing about the command or its own memory use, so there is nothing in it to debug. Do not start it again on your own, even if the work seems to need it: memory may still be short. Report what was stopped and why, and start it again only when asked. The user can turn this behavior off by starting Claude Code with CLAUDE_CODE_DISABLE_BG_SHELL_PRESSURE_REAP=1 in its environment; setting it from a shell command has no effect.</note>
</task-notification>
Claude Code stopped both dev servers and my old hardware-check command while the session sat idle, because your laptop ran critically low on memory. With 6 GB of RAM, the Next.js dev server, FastAPI and a browser together are a lot. So http://localhost:3000 isn't running right now. Nothing is wrong with the code, and I won't restart the servers myself while memory may still be short.
To run it yourself, use two terminals:
# terminal 1: backend
cd backend
.venv\Scripts\python -m uvicorn app.main:app --port 8000
# terminal 2: frontend
cd frontend
npm run dev
To use less memory while you click through it:
- Close other browser tabs and apps.
- If the dev server is still too heavy, run the production build instead: npm run build, then npm start. It uses noticeably less memory than npm run dev after the build finishes.
Or just ask and I'll start both servers again for you.
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.venv\Scripts\python -m uvicorn app.main:app --port 8000
INFO: Started server process [13272]
INFO: Waiting for application startup.
INFO: Application startup complete.
INFO: Uvicorn running on http://[REDACTED]:8000 (Press CTRL+C to quit)
INFO: [REDACTED]:53063 - "OPTIONS /api/catalog HTTP/1.1" 400 Bad Request
INFO: [REDACTED]:53063 - "OPTIONS /api/targets HTTP/1.1" 400 Bad Request
INFO: [REDACTED]:52373 - "OPTIONS /api/catalog HTTP/1.1" 400 Bad Request
INFO: [REDACTED]:52373 - "OPTIONS /api/targets HTTP/1.1" 400 Bad Request
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getting errors when trying to hit the frontend from localhost
The 400 on OPTIONS is the CORS preflight being rejected. The backend only allows http://localhost:3000 and http://[REDACTED]:3000, so the page is probably being served from a different address. I'll check what's listening.
Process 20812 is the leftover one I started at 13:55, and it's using about 280 MB of your RAM. I'll stop it and its child processes. Process 11584 is yours, so I'll leave it running.
The regex accepts any local port and rejects lookalike domains. Now I'll check the actual preflight against the updated app. This runs in-process, so your running server isn't touched.
I fixed the CORS errors, but you need to restart the backend to pick up the change. Press Ctrl+C in its terminal and run it again:
.venv\Scripts\python -m uvicorn app.main:app --port 8000
Then refresh http://localhost:3001 and the requests should go through.
What went wrong: the Next.js dev server I'd started earlier was still running on port 3000. When the session killed its shell for low memory, the server itself kept going. Your npm run dev found 3000 busy and moved to 3001. The backend only allowed requests from port 3000, so it rejected the browser's preflight checks from 3001 with the 400 errors you saw.
What I changed:
- Stopped the leftover server I'd started (process 20812, from 13:55). That also frees about 280 MB of RAM. Your own server on 3001 is still running.
- The backend now accepts any localhost port in development: localhost or [REDACTED] on any port, set in backend/app/config.py. I tested the preflight: ports 3000 and 3001 now get 200, and a foreign site like evil.com still gets 400. For production you'd set the CORS_ORIGINS environment variable to your real site address, and setting CORS_ORIGIN_REGEX= to an empty value switches the localhost rule off.
how can i use an open-weight model, building on an open-source agent harness or framework, running inference for this project?
You already have the first piece. gpt-oss-120b and Qwen are open-weight models under the Apache 2.0 licence, and the backend can switch between Groq and Ollama with one setting. The two pieces missing are an open-source agent framework and inference you run yourself. Here's how I'd add each.
1. Agent framework: add a "kitchen assistant" agent
I wouldn't make the weekly plan itself an agent. On Groq's free tier, one plan already uses most of the per-minute token allowance. An agent loop makes 5–10 model calls, so it would hit the rate limit on almost every request. The single model call plus checks in code is the right design for that part.
Instead, add a chat panel on the results page that handles day-to-day changes. That's also the most useful feature for a paying user:
- "No paneer today, what can I make instead?"
- "Eating at a dhaba tonight, what should I order?"
- "Make Wednesday cheaper."
- "I had a samosa at 4 pm, fix my dinner."
The agent gets tools that wrap code you already have, so the model decides what to do and the code does the arithmetic and rule checks:
| Tool | Wraps |
|---|---|
search_dishes(query) |
catalog.compatible_dishes, so diet rules are always applied |
day_totals(day) |
_check_days, for real calorie, protein and cost sums |
swap_meal(day, slot, dish, portion) |
edits the plan, then re-checks the day |
rebalance(day) |
balance_day |
update_pantry(add, remove) |
the profile's pantry list |
Framework: I recommend Pydantic AI (MIT licence).
- Your backend is already FastAPI with Pydantic schemas, so tools and outputs reuse the same types and need almost no glue code.
- It works with any OpenAI-compatible provider, so Groq, Ollama and vLLM all work, and so does the model fallback.
- Each user message costs about 2–4 small model calls, which fits the free tier.
The main alternative is Hugging Face's smolagents (Apache 2.0). It's more recognisably "open-source AI" to judges, but its default agent works by writing and running Python code. That's riskier and harder to control than plain tool calls for an app like this. LangGraph is fine too, but heavier than you need.
2. Inference you run yourself
| Option | Model | Cost | Fit for you |
|---|---|---|---|
| Ollama on your laptop |
qwen3:1.7b (maybe qwen3:4b) |
Free | Works for the assistant, slowly. Small models are unreliable at tool calls, and a 4B model is tight in 6 GB. Good for a "nothing leaves your laptop" demo. |
| Free Kaggle/Colab GPU + vLLM | gpt-oss-20b or Qwen3 14B | Free (Kaggle gives ~30 GPU hours a week) | Real inference you control, with the same OpenAI-compatible API. But sessions shut down, so it's for demos and recording your post, not a live service. |
| Modal (serverless GPUs) + vLLM | gpt-oss-20b | Has a monthly free credit (check the current amount) | Closest to a real product: you own the deployment and it scales to zero when unused. |
| Groq (current) | gpt-oss-120b, Qwen | Free tier | Keep it as the default and fallback. |
None of these needs code changes beyond configuration, because everything already uses the same API format: LLM_PROVIDER and LLM_BASE_URL decide where requests go.
For your post: the same open models run on Groq, on a GPU you rent or borrow, or on your laptop with no internet. Users choose the trade-off between speed and privacy, and you're never locked into one provider's pricing.
Suggested order
- The kitchen assistant on Pydantic AI, with Groq as the backend. This is the main build, about a day of work.
- Private mode: a toggle that sends the assistant to local Ollama.
- A vLLM notebook for Kaggle serving gpt-oss-20b, with a tunnel. Use it to record the "my own inference" demo and to take measurements for your post.
Should I start on step 1?
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Prize Categories
Featured categories
$200 for each winner
Render
Best Use of Render
Use Render as your project's AI runtime, to host an agent's front end, or to run Hermes or OpenClaw.
Prizes
$200 USD
+ Exclusive winner badge
Prior Labs
Best Use of TabPFN
Use TabPFN, Prior Labs' tabular foundation model, to forecast, predict, classify, or spot anomalies from historical data like a CSV. Prior Labs strongly recommends the latest version, TabPFN-3.5: sign up at platform.priorlabs.ai and accept the free non-commercial license to get your API key. Prior Labs favors projects that use TabPFN inside a wider prediction agent (with or without the MCP server).
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Best Use of Tinker
Use Thinking Machines' Tinker to fine-tune a model for a specific task, and show a clear improvement in performance, latency, or cost over a baseline.
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QualcommArduino
Best Use of Arduino
Build with an Arduino UNO Q: run a model on the board, build a physical agent that senses and acts, or optimize a model for it with Qualcomm AI Hub. Open to anyone building with an UNO Q, whether you got one at an in-person Fest or have your own.
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Best Use of DigitalOcean
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Best Use of Backboard
Build with R-CLI, Backboard's open-source terminal coding agent, compare open-weight models through a single Backboard API key, or give an open-source project's assistant memory and RAG.
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Best Use of ElevenLabs
Give an open-source agent a voice, transcribe audio for a local model, or generate narration for your demo.
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Best Use of Entire
Share the agent sessions behind your project in the write-up, or search past agent work to explain why code exists.
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Build with Copilot's coding agent, the Copilot CLI, or the new Copilot app, automate your project with GitHub Actions, or review contributors' PRs with Copilot.
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Orchestrate an agent over open models, add memory and tools to a chat agent, or build a workflow that triages GitHub issues.
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Best Use of MongoDB Atlas
Use Atlas as your data layer: Atlas Vector Search for retrieval, long-term memory for an agent, or the database behind an app built on an open-weight model.
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Best Use of Sentry Agent Tracing
Show your agent's work: how it's set up, how it performs (latency, tokens, cost), what you found, and how you debugged it. Include traces or screenshots in your write-up.
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Give an agent live web search, ground a RAG app in fresh results, or track news for an open-source project.
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.tech Domains
Best Use of a .tech Domain
Put your project live on a .tech domain you claimed through Hacktoberfest, and link to it in your write-up. Claim a free first-year .tech domain at hacktoberfest.com/my/promos (it renews at $29.99 a year).
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Make your agent durable. Wrap it in a Temporal workflow so it survives failures, retries flaky tool calls, and picks up where it left off.
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Tiger Data
Best Use of Tiger Data
Store embeddings with pgvector, run hybrid keyword and vector search for an agent, or let an agent manage a database through Tiger MCP.
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we need to use some of these as well which best fit the project
Pick about five sponsors and use each one properly, rather than ticking every box. "Best use of X" judges look for X being genuinely needed. Here's how I'd rank them for this project, given a week, a 6 GB laptop, and free tiers where possible.
Recommended
| Sponsor | Prize | What it does in ThaliWise | Effort |
|---|---|---|---|
| Render | $200 | Hosts the FastAPI backend and the kitchen-assistant agent: Render becomes the "AI runtime". We already planned to host the backend somewhere, so this is a natural fit. | Low |
| Gemma | $200 | Becomes the model for private mode and the assistant. A small Gemma can run locally on Ollama (the 1B size fits your laptop easily; 4B is tight), and a large Gemma can be served through Google AI Studio's free tier. The A/B comparison below makes a natural "Gemma vs gpt-oss" section for your post. | Low–medium |
| Mastra | $100 | Build the kitchen assistant with Mastra instead of Pydantic AI. It's TypeScript, so it lives in the Next.js app, and its tools call the existing FastAPI endpoints (dish search, day totals, rebalance). It matches the prize wording exactly: "add memory and tools to a chat agent over open models". | Medium |
| Sentry Agent Tracing | $100 | Traces every plan and agent run: latency, tokens and cost per plan. It's cheap to add, and it gives your post real numbers like "a plan costs ₹0.0X", which you need for the "someone would pay for it" argument. | Low |
| .tech domain | $100 | Claim something like thaliwise.tech for free and point it at Render. That's a 15-minute job. |
Very low |
Good if time allows
| Sponsor | Prize | Fit | Catch |
|---|---|---|---|
| MongoDB Atlas | $100 | Saves profiles, plans, a weight log and the agent's memory. Needed anyway once a real user wants their plan on their phone tomorrow. The free tier is enough. | Requires adding accounts and login. |
| Tinker (Thinking Machines) | $200 | The best story: fine-tune a small open model on Indian meal planning and show it beats the baseline. Our automatic checks (calorie band, protein, budget, diet rules, valid JSON) already give a ready-made scoring method. | The most work, Tinker may need an access request, and producing training data through Groq's free limits is slow. Treat it as a stretch goal. |
| ElevenLabs | $100 | A hands-free cooking mode that reads recipe steps aloud. Fun, and users would value it. | A side feature, not core. |
| Entire | $100 | Share the agent sessions behind the project in your write-up. The challenge already asks for that (DevRelay), so it costs almost nothing. | Depends on how they judge "best use". |
Skip
- DigitalOcean: overlaps with Render for hosting. Its GPU Droplets cost money by the hour unless you have credits.
- TabPFN: the only fit is forecasting weight from logged history, and new users have no history.
- Arduino: needs an UNO Q board. (A smart kitchen scale would be fun if you had one.)
- Temporal, SerpApi, Tiger Data, Backboard, Copilot: each would be bolted on rather than needed.
How it fits together
thaliwise.tech
│
Next.js UI + Mastra kitchen-assistant agent ── on Render (or Vercel)
│ tools call ↓ └─ Sentry traces (tokens, latency, cost)
FastAPI: targets, catalog, plan, balancer ── on Render
│
Open-weight models: gpt-oss-120b (Groq) · Gemma (Google AI Studio) · Gemma on local Ollama (private mode)
│
MongoDB Atlas (optional): profiles, plans, agent memory
If you want the strongest Render entry, host the frontend on Render too instead of Vercel, so the whole product runs there.
Accounts you'll need to create
I can't sign up for these on your behalf:
- Render
- Sentry
- Google AI Studio, for a Gemma API key
- Claim the .tech domain at hacktoberfest.com/my/promos
- Optional: MongoDB Atlas, and a Tinker access request
Each key goes into .env like the Groq one. Before writing code I'll also check which Gemma version and which free services are available right now.
Should I go with the five recommended ones? If so, I'd start with Gemma in the model setup and the Mastra assistant, since that's the main feature, then add Sentry and deploy to Render.
got the .tech domain as kilobite.tech, sentry is setup in visual studio and created render account with the github account details, lets park the idea of using mongodb and tinker for now, and created the gemma key as well, so whats next now with the app
Good progress. Next I'll wire Gemma in, since it touches the model setup that everything else uses. Then the Mastra assistant, then Sentry, then deploying to Render on kilobite.tech. First I'll check what's in .env (variable names only) and what Gemma models your key can access.
Now the LLM client: it adapts to each provider and keeps the fallback logic.
Next I'll update the planner, schemas and API: /api/models lists the available choices, and a plan request can name one.
Only gpt-oss shows for now, which is right. Next I'll add the model picker to the frontend's last step.
The model picker compiles and passes checks. I'll update .env.example with the new variable names.
I'll save the project context so future sessions pick up where we are.
I've built the model picker, but I can't test Gemma until its key is in .env. Your key isn't there yet: right now the file only has the Groq key and your Sentry notes.
What's done
-
Backend: supports several model providers side by side, all serving open-weight models:
- gpt-oss-120b on Groq (default, with Qwen as the rate-limit fallback)
- Gemma through Google AI Studio
- Gemma on your laptop through Ollama
Each option only appears once it's usable: its key is set, or for local Gemma, Ollama has the model downloaded. The code also handles Gemma on Google's API not accepting system prompts or JSON mode.
- New /api/models endpoint: plan requests can now name the model to use.
- Frontend: step 4 has a model picker ("Which open model should plan your week?"), and the results page shows which model made the plan, with a 🔒 label when it ran entirely on your computer.
- Type-checking and linting pass. .env.example lists all the variable names.
What I need from you
-
Gemma key: add a line
GEMINI_API_KEY=<your key>to.env. -
Sentry DSN: in Sentry, create a project (Python, FastAPI) under your org. Copy its DSN from Settings → Projects → your project → Client Keys and add a line
SENTRY_DSN=<dsn>to.env. The notes you left in.env(org slug, run code) aren't needed by the app; you can delete them. -
Optional, private mode: run
ollama pull gemma3:1b(about 800 MB). The 1B size is small enough for your 6 GB laptop. It's for the offline demo; its full-week plans will be noticeably weaker. -
Restart the backend after editing
.env. - Naming: the domain is kilobite.tech but the app is called ThaliWise. Should I rebrand the UI to Kilobite so the name matches the domain?
Once the Gemma key is in, I'll check which Gemma versions your key can access, run a plan on Gemma and compare it with gpt-oss on the same checks (calories, protein, budget, diet rules). Then I'll build the Mastra kitchen assistant, add Sentry tracing, and deploy to Render on kilobite.tech.
saved the key in the file
The backreference got lost going through the shell. I'll fix that line directly.
It's an invisible control character (the mangled \1), so I'll replace the whole line by line number.
All parsing cases pass, and Gemma now appears in the picker. Next, a real week plan on Gemma with the same test profile, scored by the same checks as gpt-oss.
Now the split-up planner. First I'll mark which providers use it.
Now the planner itself. I'll split the prompt into reusable context and task instructions, then add the chunked path.
Now the chunked draft functions, and the switch in generate_plan.
Each Google call can now produce up to about 5,000 tokens at about 40 per second, which could exceed the 120-second timeout. I'll give Google more time, then run the Gemma plan again.
The heredoc turned my \n into a real line break again. I'll fix that line with a direct edit.
Now a day call ran out of room, even with about 6,700 tokens for 3 days. That's far more than 3 days should need, so I'll look at the raw output to see where the tokens go.
I'll move the call and parsing into a helper that retries once with double the room when the output was cut off.
The parser handles all the cases. Running the Gemma plan again:
Gemma 4 now works in the app, and the default gpt-oss path still works after the refactor: 14 seconds, all 7 days within the calorie range. Restart the backend to pick up the changes, and Gemma will appear in step 4's model picker.
Gemma in practice:
- Your key gives access to Gemma 4, not Gemma 3. The app uses gemma-4-26b-a4b-it and falls back to gemma-4-31b-it, which kept failing in my tests because Google said it was overloaded.
- Google's free tier is slow and sometimes unavailable. It streams about 40 tokens per second, versus 11–14 seconds for a whole plan on Groq. In two full runs, one completed in 146 seconds (all 7 days within the calorie range, 10 of 10 recipes) and one failed because Google returned 500/503 errors. When that happens the app now says "Gemma is overloaded right now. Try again shortly or pick another model." It doesn't hang.
What I changed to make it work:
- Split-up planning for slow models. One outline call, then 3 day-group calls and the recipe calls in parallel. gpt-oss keeps its single call.
- Gemma thinking control. Gemma 4 always "thinks" before answering, and the API won't switch it off. One instruction cut a call from 82 seconds to 24. Separately, asking it to "adjust until within 5%" made it re-add calories for over 3 minutes; it now aims roughly and leaves the exact totals to the balancer.
- Sturdier parsing: handles Gemma's <thought> blocks, answers wrapped in arrays, and JSON split across several objects. A call that runs out of room retries once with more.
- Honest wait times: the model picker and loading screen show each model's typical time (10–30 seconds for gpt-oss, 1–3 minutes for Gemma).
Known issue: in today's gpt-oss run, protein came in under target on 6 of 7 days (75–79 g against 96). Calories were already near the limit, so the balancer couldn't add curd or eggs without going over. The fix is to swap a roti for a protein item instead of only adding food. I'll do that alongside the assistant.
For your post: Gemma's 26B mixture-of-experts model makes a usable full-week plan on a free tier because the work is split into small parallel tasks with code checking the numbers. The same split is what makes small local models workable.
Next step: the Mastra kitchen assistant. Gemma fits it well, since short replies hide the slow speed; gpt-oss stays the default for full plans. I still need your answer on one thing: should I rename the app to Kilobite to match kilobite.tech?
the name of the app should be meal-planner, and lets not only focus on indian meals, it should be generic. there are some changes i would like to suggest.
1. page 1 is good, in page 2 instead of selecting the location let it be a user input which can be used to then load the data about that region, like russia, france, lyon, london, manchester, delhi, baramulla, .. so location can be generic. same with food culture you grew up with as well, the eater field is good, maybe add more options there
2. for the page which asks what your daily food intake looks like, the options from pre-filled seems like restricting the input, instead make it more natural language, or the app would expect a natural language answer in an input field. i would suggest add an agent here to take the food restrictions in and calculate or even give suggestions if the agent thinks the fields are not filled properly.
3. same with the page for whats in your pantry, instead of choosing from a selection of options, the user would give what uaually is available in their pantry to start with. dont make the user select the agent, it should be on the analysis of the types of inputs the agent is getting.
4. add routing in Ui and home page buttons as well, right now its difficult if someone wants to restart the plan.
5. add the agent chat during and after plan is added, and only expect diet and food related questions, rest of them should be sorry, not what the chat is to be used for.
6. add a knowledge base for the region, food habit type to make it easier and more faster for the agent to get the details
This is a big redesign, so here's the plan before I build:
- Generic, not Indian-only. A knowledge base of curated cuisine profiles (staples, reference dishes with nutrition values, meal times, currency, typical budgets), including your examples: Russia, France (Lyon), UK (London, Manchester), North India (Delhi), Kashmir (Baramulla) and more. A location that isn't in it gets a profile generated once by the model, then saved to the knowledge base, so the next lookup is instant.
-
Agents built with Mastra (sponsor):
- An intake agent reads a free-text description of a typical day, matches foods against the knowledge base, estimates calories, and asks follow-up questions when something is missing or contradicts the diet.
- A pantry agent turns free text into a pantry list.
- A chat agent with a guardrail that only answers food and diet questions.
- No model picker. A router picks the model from the task and the input (for example, non-Latin scripts go to Gemma, which handles many languages; short extraction goes to a fast model), and the UI shows which model handled each step.
-
Routing: a home page,
/plan/...step pages, and a results page with "Start over" and "Continue" buttons.
I'll start by checking Mastra's current API, since I'll be building against it.
These packages release almost daily. As a supply-chain precaution I'll pin versions that are at least two weeks old, and check which ai version Mastra expects.
Installed. Before writing agent code I'll read Mastra's type definitions for this version: the agent constructor, structured output, model fallbacks, and streaming to the AI SDK chat UI.
Mastra ships its own documentation inside the package (dist/docs). I'll check the references for structured output and guardrails/processors.
The built-in classifier needs a special evaluation model, so a custom input processor that calls gpt-oss-safeguard-20b with a written policy is simpler. I'll check the custom-processor interface.
I need to know what a tripwire turns into in the stream sent to useChat, so the refusal reaches the user as a normal message rather than an error.
Now llm.py. chat_json will take an ordered model list that can span providers, instead of one provider's choice.
Now the schemas. I'll make them generic: free-text location with resolved region, any currency, free-text typical day and pantry, and diet styles.
Now nutrition: switch to WHO BMI categories, since the app isn't India-only anymore, and add macro adjustments for diet styles (keto, low-carb, high-protein) applied at plan time.
Now the API: options, a region lookup and status, knowledge-base dish lookup (for the agents), targets, plan, and a re-check after the chat agent swaps a meal.
Before writing the Mastra model registry, I'll check which provider options @ai-sdk/groq and @ai-sdk/google accept (reasoning effort, thinking).
Both providers expose what I need. Now the Mastra side, starting with the model registry and the router. It mirrors the backend's task-based routing for the agent tasks (intake, pantry, chat, guard).
Next, the chat agent's guardrail. I'll confirm the processor argument types first.
I'll add a helper to the model registry that returns plain AI SDK model objects. The guard calls the classifier directly rather than as a full agent.
Calling abort() inside the try is fragile, because the tripwire it throws has to get past my catch. I'll restructure so classification happens in the loop and abort() runs outside it.
Now the three Next.js route handlers that run the agents. I'll check how to get an agent from the Mastra instance and where the answering model's ID appears on the result.
I have the result shape (object, response.modelId). Writing the route handlers now: intake and pantry return JSON, and chat streams.
Before the UI, I'll type-check the agent layer on its own. The old UI components still reference removed types, so I'll filter the output to the new files.
Only one error: the Gemma entry has no providerOptions, so TypeScript can't see the field on the combined type. I'll widen the type.
The agent layer type-checks cleanly. Now the UI. I'll update StepBasics for the new store (its props change from the old form type) and switch its BMI scale to WHO categories.
Now the step page (one dynamic route for all four steps, with linked progress and Back/Continue) and the result page (loading screen, error and retry, or the plan).
Type-checking and linting both pass on the whole frontend. Before testing live, I'll see which servers are running, since the backend changes need a restart.
The shell turned \n into a real line break again. I'll fix the function with a direct edit.
Type-checking and linting pass. I'll make sure every route renders on your running Next.js server, and that an unknown step returns 404.
Every route renders, and /plan/bogus correctly returns 404. Last: update the README, .env.example and my project notes.
Final check: scan the project for either key's value. Nothing is printed except file names.
All six changes are built, and the app is now meal-planner and works for any cuisine. Restart the backend to see them: press Ctrl+C in its terminal and run .venv\Scripts\python -m uvicorn app.main:app --port 8000 again. It's still running the old code, so the new knowledge base and planner aren't live yet; Next.js has already picked up its changes. I tested the backend, the agents and that every route loads, but I couldn't click through the UI in a browser, so please try the full flow once.
Your six changes:
-
Location and food culture are free text. You type any place and press Find:
- Curated places (Lyon, Manchester, Baramulla, Moscow/Москва, Delhi, Tokyo and others) match a knowledge base of 14 cuisine profiles instantly.
- Other places are mapped by a fast model (Columbus → American in 0.8 seconds). If the cuisine is new, Gemma 4 writes a profile in the background and saves it. Kraków took 69 seconds the first time and was instant afterwards.
- Eater types: now 10, including pollotarian, flexitarian and paleo, plus eating styles like keto, high-protein and Mediterranean. Prices are in local currency.
- The typical day is plain text with an Analyze button. The intake agent turns it into editable meals with calorie estimates, flags diet conflicts and asks up to three follow-up questions. In testing it flagged that "sometimes chicken curry" conflicts with a vegetarian diet.
-
The pantry is plain text too. After one fix it extracted all 14 items exactly, picked up the equipment mentioned, and suggested common local basics. Before the fix it invented a banana and missed ghee and mustard oil.
- No model picker: a router chooses the model from the task and the input. Short extraction goes to Qwen, non-Latin text to Gemma 4, the full plan to gpt-oss-120b, and new knowledge-base profiles to Gemma 4. Each screen shows which model handled it and why.
-
Pages and navigation: a home page (Build my week / Continue / View my plan / Start a new plan),
/plan/basics,/food,/day,/kitchenand/plan/result. The header always has a Start over button that asks for a second click. Answers are saved in the browser. -
Chat on every plan page, including while a plan is generating:
- Off-topic questions are blocked in about 1 second by a guard using gpt-oss-safeguard-20b. "Write me a Python function" got: "Sorry — I can only help with food, diet and your meal plan."
- Food questions get tool-backed answers. Asking to make Wednesday's dinner cheaper produced a swap card with an Apply button in 4 seconds; applying it re-checks that day's totals.
-
Knowledge base:
backend/kb/cuisines.jsonholds 14 hand-written profiles. Model-written profiles go inbackend/kb/generated/, which I added to.gitignore. The agents use it to ground their calorie numbers.
Test results:
- Lyon plan: a vegetarian with a peanut allergy who avoids mushrooms got a full week in 12.8 seconds. Every day was on target for calories and cost €6–7 against a €10 budget, with French food and nothing forbidden.
- Protein still came in under target in that run. I've since capped the high-protein target at 30% of calories, and the balancer can now swap out a portion of bread or rice to make room for protein. I haven't re-run that plan since those changes.
- Type-checking, linting and all routes pass, and no API key appears anywhere outside .env.
Not done yet:
- Sentry: there's still no SENTRY_DSN in .env, so tracing isn't wired in.
- Offline mode only covers planning; the agents still use the hosted models.
- 12 npm audit warnings came with the new dependencies. I haven't looked into them yet.
Once you've clicked through, the next steps are Sentry tracing and deploying to Render on kilobite.tech.
few fixes before that. change the color code from orange to something better. next is in the section of where do you live and food culture you grew up with, only keep important cities or culture as a prefilled text, not something like baramulla, or lyon, or manchester. the other chagne is for the same page for eatign style and religious rules and allergies, keep these options but give an option to add custom data as well, this is currently restricting the options to the user
Three fixes: a new colour scheme, mainstream examples on the "Your food" page, and custom entries for eating styles, rules and allergies.
Colours: orange appears in two places, the main accent and the reddish-orange used for warnings. I'll switch the accent to a deep teal, warnings to rose, and success to an olive green so it stays distinct from teal, with matching dark-mode values.
Now a reusable chip group that lets users add their own entries. Preset options stay as toggles; anything typed is added as a selected chip with a remove (×) button.
All three fixes are done. Type-checking and linting pass, both pages load, and no orange colour values are left in the code. I haven't seen the new colours in a browser, so take a look.
Colours: the accent is now a deep teal instead of orange. "On target" indicators are olive green, so they stay distinct from the teal, and warnings are rose instead of reddish-orange. Dark mode has matching versions. All the colours live in one block in
globals.css, so they're easy to tweak.-
Mainstream examples on the "Your food" page:
- Location examples: London, New York, Mumbai, Paris, Tokyo, Mexico City and Dubai. The input placeholder uses London, Mumbai, Paris and New York.
- Food-culture suggestions: now a fixed list of broad cuisines (Indian, Chinese, Italian, Mexican, Middle Eastern, Mediterranean, Japanese, Korean, Thai, French, British, American, African, Latin American). Before, it listed every cuisine in the knowledge base, including niche ones like Kashmiri. Users can still type anything.
- Home page: I made the same change to the "Type any city" line there, since it listed Lyon, Manchester and Baramulla too.
- Niche places still work when typed; they're just not offered as examples.
-
Custom entries for eating styles, religious rules and allergies: the preset options stay as toggles, and each section now has a "+ Add your own" field. Press Enter or click away to add an entry; it appears as a selected chip you can remove with ×. Typing a name that matches a preset (like "Halal") selects the preset instead of adding a duplicate.
- In the planner: custom rules were previously dropped from the planner's prompt; now they're included word for word. The prompt says to treat custom restrictions and allergies as absolute, and the balancer won't add a food that matches a custom allergy (for example "kiwi").
- In the agents: the intake and chat agents already receive these lists, so they see custom entries too.
- One limit: a custom rule like "no food after sunset" is enforced only by the model following the prompt. The code can check preset allergens, like peanuts, against ingredient tags, but it has no way to check free-text rules.
The backend change only takes effect after you restart the backend; the frontend has already reloaded. After that, we can move on to Sentry and deploying to Render.
added sentry_dsn to .env file, what is needed for render?
Sentry first, since the DSN is in. I'll check the variable names in .env (not values) and whether Mastra has a built-in Sentry integration for agent tracing.
Render deploys from a GitHub repository, and this project isn't a git repository yet. I'll check whether the GitHub CLI is installed.
Your Sentry DSN didn't save. .env still only has API_KEY and GEMMA_API_KEY (last saved at 15:26), and I searched the whole project and found no DSN anywhere, so it isn't sitting in a file that could get committed. Add it to .env as one line, SENTRY_DSN=https://…, then save.
What Render needs
I've written render.yaml, a Render Blueprint that defines both services on the free plan in the Singapore region, the closest to India:
| Service | What it runs | Address |
|---|---|---|
meal-planner-api |
FastAPI (uvicorn, Python 3.13.3, health check /api/health) |
api.kilobite.tech |
meal-planner-web |
Next.js + Mastra agents (npm ci && npm run build, npm start, Node 22) |
kilobite.tech and www.kilobite.tech
|
The keys in the Blueprint are marked as secrets with no values, so Render asks you for them when you apply it and they never go into the repo. CORS is limited to your domains.
What's still needed, in order:
1. A GitHub repo: Render deploys from GitHub. This folder isn't a git repo yet. Your GitHub CLI is already logged in as Karan9bhat, so I can create the repo, check that .env is excluded, commit and push, once you say yes (below).
2. Apply the Blueprint: in Render, go to New → Blueprint, pick the repo, and paste each secret: API_KEY, GEMMA_API_KEY and SENTRY_DSN, for both services.
3. DNS for kilobite.tech, at your .tech registrar (the dashboard where you claimed it). Render shows the exact records under each service's Settings → Custom Domains. Usually that's:
- kilobite.tech: an A record (or ALIAS) to Render's IP
- www.kilobite.tech and api.kilobite.tech: CNAMEs to the *.onrender.com names Render gives you
Render then issues the HTTPS certificates automatically.
Free-tier things to know:
- Sleep: services sleep after about 15 minutes idle, so the first visit afterwards can take about a minute. Open the site yourself before showing it to anyone or recording your demo.
- Memory: each service gets 512 MB, which should be enough for both.
- Knowledge base: profiles generated by Gemma (like the Polish one) are lost on each redeploy, because there's no persistent disk on the free plan. They're rebuilt the first time someone asks again.
- Untested build: I haven't run a production build (npm run build) yet. With about 800 MB of RAM free and your dev servers running, it could run out of memory here, so Render's build will be the first real test. Alternatively, stop your dev servers and I'll run it locally first.
Sure, what would you like to clarify about the GitHub repo or the Render setup? For example: the repo name or visibility, whether you'd rather push it yourself, or how the deploy works.
repo is already created called meal-planner and added as a project to sentry. also i found an issue with the frontend, for INR the slider is fine to get food budget but when looking for dollars, its broken - minimum per day is 1750 dollars which isnt correct. also the currency should change according to the region that is selected, like for Pune its INR, for new york its dollar, for london it should be pound, for germany it should be euros, for brazil it should be the brazilian money, etc.
Before changing anything, I'll look at what your session actually resolved. Your backend writes looked-up places and generated profiles to backend/kb/generated/, so I'll check their currencies and budget ranges.
I'll store a budget range on the region reference itself, so every place carries a budget in its own currency, whatever cuisine profile it maps to.
[Request interrupted by user for tool use]
add these details related to region, type of food preference choices which generates the diet plan, and rest into a knowledge graph using mongo db
I'll stop there; none of the currency fix has been applied. First I'll check schemas.py, since it changed on disk even though my edit was rejected.
The backend part of the currency fix is fully in place and the backend still loads. The frontend part isn't done yet: currency-code formatting (CLP 1,750 instead of "$1,750") and resetting the budget when the region's currency changes. Nothing is broken in the meantime, because the new fields are optional. I'll finish that alongside the MongoDB work unless you say otherwise. I also see SENTRY_DSN is in .env now (saved 17:41), so Sentry is unblocked.
Knowledge graph on MongoDB: proposed design
Nodes and edges in two collections (kg_nodes, kg_edges), queried with MongoDB's $graphLookup for graph traversal. That's a knowledge graph inside Atlas, and it fits the sponsor category ("Atlas as your data layer").
Place ─IN→ Country ─USES→ Currency
└─EATS→ Cuisine ─HAS_DISH→ Dish ─CONTAINS→ Tag (dairy, gluten, pork, peanut…)
└─STAPLE→ Ingredient
DietType / Restriction / Allergy ─EXCLUDES→ Tag
DietStyle ─ADJUSTS→ Macro (keto → carbs, high-protein → protein)
- Food preference choices (diet types, styles, rules, allergies) become nodes linked to the ingredient tags they exclude. One traversal from "vegan + halal + peanut allergy + Lyon" answers "which French dishes can this person eat?", and the planner, agents and balancer would all use that same query.
- Region details (place → country → currency/budget → cuisine → dishes) live in the graph. The current JSON knowledge base becomes the seed and an offline fallback when MongoDB isn't reachable.
- Gemma-generated profiles (Polish, Chilean…) are saved to Atlas, so they survive Render redeploys. Render's free tier has no persistent disk, so today they're lost on every deploy.
What I need from you:
- A MongoDB Atlas free (M0) cluster, with a database user.
- Its connection string in .env as MONGODB_URI=[REDACTED][REDACTED]/0`. Render's free tier has no fixed IP address, so the database has to accept connections from any IP and relies on the password for protection.
- In Atlas, under **Network Access**, allow
One decision matters before I build:
Before the refactor, I'll record what every diet and rule currently excludes, so I can prove the hierarchy version produces exactly the same sets.
I'll make money() take the currency code (with the symbol as a fallback) and update every caller. The budget will now follow the region's own budget and currency.
I've built the MongoDB knowledge graph and finished the currency fix. The graph hasn't touched Atlas yet, because there's no MONGODB_URI in .env. Until there is, the app uses the JSON knowledge base with exactly the same rules, so nothing is broken.
Knowledge graph
As you chose, it holds reference knowledge only; user answers and plans stay in the browser. It's stored as nodes and edges in kg_nodes and kg_edges (code in backend/app/graph.py):
- Region details: place → country → currency, place → cuisine → dish → ingredient tags, each cuisine's staples and its price currency.
- Food preference choices: diets, rules, allergies, eating styles and equipment are all nodes. A diet now excludes broad groups that the graph expands, for example vegan → animal product → meat → red meat → beef, and a dish is allowed only if none of its ingredient tags are excluded.
- Persistence: places looked up and cuisine profiles written by Gemma (Polish, Chilean, Canadian, Emirati…) are saved in Atlas, so they survive Render redeploys. The free tier's disk is wiped on every deploy.
- Demo endpoints: GET /api/graph/stats, and POST /api/graph/explain, which shows which rule blocks which dish.
I checked that the new hierarchy bans exactly the same ingredients as the old flat lists for all 10 diets and 7 rules. With no MONGODB_URI, the backend falls back to the JSON knowledge base cleanly.
What you need to do:
1. In Atlas, create a free M0 cluster and a database user.
2. Under Network Access, allow [REDACTED]/0. Render's free tier has no fixed IP address, so the password is the protection.
3. Add the connection string to .env as `MONGODB_URI=[REDACTED] then restart the backend. On startup it seeds the graph in the background.
Then I'll check that the graph traversals give the same answers as the fallback, and confirm on a redeploy that generated profiles survive.
Currency fix (finished)
- Every place gets its own currency and budget: Pune ₹, New York $, London £, Germany €, Brazil R$, Toronto CA$, Dubai AED, Santiago CLP. Places that eat a known cuisine but pay in another currency (Toronto) use their own prices.
- Unambiguous money display: Chilean pesos now show as CLP 1,750, not "$1,750". Mexican, Canadian and Australian dollars get prefixes too, while ₹, £, € and R$ stay as they are.
- The budget resets when you switch to a region with a different currency, and the slider's step scales with the currency: 0.5 for £/€/$, 10 for ₹, 100 for pesos or yen.
Type-checking and linting pass, and I checked the currency for each place in-process. I haven't seen the budget slider working in a browser, so check it on Your kitchen.
Repo and Sentry
-
Repo: the GitHub repo exists, but this folder isn't connected to it yet. Say the word and I'll link it and push, after confirming
.envandkb/generated/are excluded. -
Sentry: your DSN is in
.envnow, so I'll wire up tracing next. -
Render:
render.yamlnow includesMONGODB_URItoo.
what i have given for connection string is certificate based authentication, get the certificate to establish the connection. access to [REDACTED]/0 is given by default
With X.509 certificate authentication, the connection string has no password; the client proves who it is with a PEM file holding the certificate and private key, which Atlas gives you once when the database user is created. I'll check what's in .env (printing only the connection string's option names, not values) and look for a downloaded .pem file (locations only; certificate contents are never printed).
The backend now handles certificate login. With your current setup it reports exactly what's missing ("…uses certificate (X.509) auth but no certificate was found at …/backend/certs/mongodb-x509.pem…") and keeps running on the JSON knowledge base. I created the backend/certs/ folder, and git is set to ignore it.
Getting the certificate (one-time, in the Atlas website)
The file contains a private key, so treat it like a password. Atlas only shows it at the moment it's created:
- In Atlas, open your project, then Security → Database Access.
- Find the user with authentication method Certificate:
- Existing user: Edit → Download Certificate. This issues a new certificate; earlier ones stay valid until they expire.
-
New user: Add New Database User → Certificate, enter the username and an expiry (for example 12 months), give it Read and write to any database (or just the
meal_plannerdatabase), and tick the download option.
- Your browser saves a file like
X509-cert-….pem. Rename it tomongodb-x509.pemand move it intobackend\certs\. - Restart the backend.
Then tell me and I'll check the connection, seed the graph, and confirm the graph traversals match the JSON fallback.
Command-line alternative: if you'd rather not use the website, install the Atlas CLI and run these in your own terminal (the login opens a browser):
! atlas auth login
! atlas dbusers certs create --username <your-x509-user> --monthsUntilExpiration 12 > backend/certs/mongodb-x509.pem
For Render later
render.yaml now sets MONGODB_CERT_PATH=/etc/secrets/mongodb-x509.pem. In the backend service, go to Environment → Secret Files, add a file named mongodb-x509.pem, and paste the certificate's contents. Render mounts it at runtime, so it never goes into the repo.
got this in UI logs.
✓ Compiled /api/agent/intake in 1896ms
No storage configured on Mastra — falling back to an in-memory store. In-memory storage is not durable: all data is lost on restart, and it is not safe for production. Configure a persistent storage adapter (e.g. @mastra/libsql, @mastra/pg, @mastra/cloudflare). See https://mastra.ai/docs/storage
restarted and app is working now. whats next for this? also create multiple test cases for the application with different regions and outputs, and get the data for those regions and cuisines from the internet and run comparison tests on the results
Three things here: the Mastra warning, what's next, and a test suite. First I'll check whether the knowledge graph actually connected after your restart, and what Mastra offers for storage.
[Request interrupted by user]
also one more thing, the meals for each day are exactly same, generate meal plans which will be different but should reach the goal, there shouldn't be repeativeness
I'll add that to the list. Repeated meals matter most for real users, so I'll do them first, then the Mastra warning, the test suite with internet-sourced reference data, and what's next.
First, the knowledge graph status after your restart, and an actual plan from your running backend to measure how repetitive it is.
I'll put the variety check in its own module, the same way balance.py handles calories: plain counting code, no model involved.
The same shell problem again: \n became a real line break. I'll fix that line directly.
The planner loads with the new rules. Next, the backfill: on startup the backend now also pushes local-only Gemma profiles to Atlas. After that, a live test of variety on two profiles.
InMemoryStore is exported from @mastra/core/storage, and passing it explicitly switches the warning off: Mastra only warns when it falls back on its own. I'll check its constructor.
The bulk file is 66 MB of JSON, which could use 500 MB or more of RAM to parse, and this laptop has little to spare. I'll check free memory before loading it.
The index has 5,432 USDA survey foods. Now I'll find candidate matches for knowledge-base dishes across cuisines, then pick only true equivalents by hand.
The dataset uses the official name Türkiye, so I'll update that entry.
The fixtures are built: 27 USDA nutrition references and 22 country currencies. Now the tests, with pytest pinned in a separate dev requirements file.
Now the balancer: up to 3 trims, of the starch the day actually contains.
While that runs, I'll add a testing section to the README.
The evaluation is still running in the background. I'll report its results as soon as it finishes.
i also noticed that its only gitting groq api, and not the gemma one, so how to make sure the testing against both api's is done. and what are the steps for deploying to render?
The Groq-only behaviour is the router working as designed: the full plan goes to gpt-oss-120b on Groq first, because it does a week in about 12 seconds versus 1–3 minutes for Gemma. Gemma is only used for writing new regional profiles, for non-Latin input, and as a fallback when Groq is rate-limited. So the tests so far have mostly exercised Groq.
To test both providers deliberately, I'll add:
1. A route override: the environment variable ROUTE_OVERRIDE (for example plan=gemma-4-26b), and a --models flag on the evaluation, to force a chosen model chain.
2. A live provider test that calls every configured model directly (gpt-oss-120b, Qwen, Gemma 4 26B-A4B, Gemma 4 31B), so a broken key or retired model fails the test.
3. A Gemma evaluation run on a subset of profiles, giving a side-by-side gpt-oss vs Gemma comparison (handy for the Gemma prize write-up).
The evaluation running in the background has already loaded its code, so editing files now won't affect it.
[Request interrupted by user]
also i dont see anything going to sentry at all, is something broken there or the setup is still not completed?
The regional evaluation has finished. I'll finish the Sentry wiring first, then go through its results.
Type-checking and linting pass. Next.js restarts on its own when its config changes, so I'll send one real pantry-agent request through your running server to produce an agent trace:
Sentry is wired into both halves. Now the regional evaluation results:
I'll keep the first run's report as report_run1.md, since the re-run overwrites report.md, then start the re-run in the background.
While that runs, I'll set SENTRY_ENVIRONMENT=production for both services in render.yaml, so deployed traces are kept apart from your local ones.
Sentry was never broken; the code to use it was just never written. It's in now and data is arriving, and testing now covers both Groq and Gemma. Details below, then the Render steps.
Sentry
I connected Sentry in both halves of the app:
- Backend: each plan shows up as one "planner" run. It contains every model call (model name, latency, token counts), the draft / variety / balancer steps, and the background job that writes new regional profiles.
- Agents: the intake, pantry and chat agents send traces through Mastra's Sentry exporter, including tool calls and token usage. Agent errors are also reported as issues.
- Privacy: prompt and answer text is never sent, only the shape of each run, timings, tokens and errors.
It's working: a test from the backend arrived (event id 3b39f664…), and one live pantry-agent request went through your running server afterwards. In Sentry, look under Issues for "meal-planner backend: Sentry connectivity check" and under Traces / AI Agents for the runs.
Restart the backend so normal use is traced too; the frontend restarts itself after config changes. The Mastra "no storage" warning also disappears with this change. In-memory storage is intentional, since agents keep nothing between requests.
Testing both Groq and Gemma
The router sends plans to Groq first because it's faster (about 12 seconds versus 1–3 minutes for Gemma). Gemma is used for new regional profiles, non-Latin input, and as the fallback. To test both deliberately:
- pytest tests --live now calls every model directly. Result: gpt-oss-120b, Gemma 4 26B-A4B and Gemma 4 31B passed. Qwen was skipped because Groq reported it busy while my evaluation was using the same quota; I'll recheck it.
- Forcing Gemma for a whole run: ROUTE_OVERRIDE="plan=gemma-4-26b,gemma-4-31b" makes every plan in that run use Gemma. For example, ROUTE_OVERRIDE="plan=gemma-4-26b" python -m evals.region_plans runs the regional evaluation on Gemma. You stopped my --models flag edit, so this environment variable is the way for now; just note the run overwrites evals/report.md.
Regional evaluation and fixes
First run, 7 of 10 profiles produced a plan (report saved in evals/report_run1.md):
- Calories on target 49/49 days, budget 49/49, currency 7/7, and no real diet violations. The two flagged meals were goat's cheese, which the scanner misread as goat meat; that's fixed.
- 85% of meals used the region's own dishes. Baramulla was the exception at 57%, drifting to a tofu stir-fry and soya chunk curry. I haven't fixed that.
What I fixed from the results:
- Failed plans: the 3 failures now fall through to the next model, and the plan format tolerates small slips like "350 kcal".
- Plant-based protein: the balancer now uses the most protein-dense food that fits the diet. On a low-protein test day the vegan version improved from 52 g to 76 g and passed. The vegetarian version reached 72 g, just under the 74 g pass line.
A re-run of the six weak or failed profiles (Pune, Chennai, London, Tokyo, New York, Moscow) is running in the background; I'll report when it's done.
Deploying to Render
-
Push the code to your
meal-plannerrepo. The.gitignorealready excludes.env,*.pem,backend/certs/and the generated knowledge-base files. I can link the repo and push once you say so, after checking that nothing secret is staged. -
In Render, go to New → Blueprint and choose the repo. It reads
render.yamland creates two services in Singapore on the free plan:meal-planner-api(FastAPI) andmeal-planner-web(Next.js + agents). -
Fill in the secrets it asks for.
- Backend:
API_KEY,GEMMA_API_KEY,SENTRY_DSN,MONGODB_URI. - Frontend:
API_KEY,GEMMA_API_KEY,SENTRY_DSN.
- Backend:
-
Upload the MongoDB certificate: on the API service, go to Environment → Secret Files, add a file named
mongodb-x509.pemand paste in your certificate. It's mounted at/etc/secrets/mongodb-x509.pem, the pathrender.yamlalready points to. -
Deploy, then check
https://meal-planner-api.onrender.com/api/health. It should show"graph": trueand your models available. -
Point kilobite.tech at Render: each service's Settings → Custom Domains shows the DNS records to add at your .tech registrar:
- an A or ALIAS record for
kilobite.tech - CNAME records for
wwwandapi
- an A or ALIAS record for
HTTPS certificates are issued automatically once DNS resolves.
7. Keep the address config in sync: the browser reaches the backend at https://api.kilobite.tech, and that address is baked into the frontend when it builds. If you deploy before DNS is ready, temporarily set NEXT_PUBLIC_API_URL and BACKEND_URL to the API's onrender.com address, then change them back and redeploy.
Free-tier caveats:
- Both services sleep after about 15 minutes idle, and the first visit afterwards takes about a minute. Open the site before demoing.
- The frontend's production build hasn't been run yet. Render's build will be its first real test, and I'll fix anything it reports.
<task-notification>
<task-id>ba2h5ch7c</task-id>
<tool-use-id>toolu_01FUp5p4vQU8LE9fYgKz9WCk</tool-use-id>
<output-file>~\AppData\Local\Temp\claude\C--Users-bhat--OneDrive-Documents-Karan-Dev-oktoberfest2026-week2\a5896de6-5c0f-4f40-bb97-dbdaa37a8c1f\tasks\ba2h5ch7c.output</output-file>
<status>completed</status>
<summary>Background command "Re-run evaluation for the six failed or weak profiles" completed (exit code 0)</summary>
</task-notification>
All offline tests still pass. Re-checking the two profiles these fixes target, Chennai (failed with 413) and Moscow (repetitive), after a pause so Groq's per-minute budget has reset:
Both now pass every check:
| Profile | Model | Calories | Protein | Budget | Distinct dishes | Repetitive days | Regional dishes |
|---|---|---|---|---|---|---|---|
| Moscow · pescatarian | gpt-oss-120b, 12.6 s | 7/7 | 7/7 | 7/7 | 25 (was 9–10) | 0 | 96% |
| Chennai · eggetarian, no onion/garlic | Gemma 4, 245 s | 7/7 | 7/7 | 7/7 | 20 | 0 | 69% |
Chennai shows the fallback working under real pressure. My evaluation runs had used up Groq's free allowance ("try again in ~790 s"), so gpt-oss and Qwen were both unavailable and Gemma 4 produced a complete, valid week instead of an error. It took 4 minutes, but every check passed.
Combined across both runs, all 10 profiles now produce plans:
- Calories on target 70/70 days.
- Correct currency for every region.
- No real diet violations.
- 17–27 distinct dishes per week.
The reports are in backend/evals/report_run1.md, report_run2.md and report.md.
Still open:
- Plant-based protein: usually within 10% of target, with up to 2 of 7 days below the pass line.
- Baramulla: 57% regional dishes in the first run.
- Groq's free quota: my test runs used much of it, so give it some time before you demo, or Gemma will answer slowly instead.
Remember to restart the backend to pick up today's fixes and Sentry tracing. When you're ready, say the word and I'll link and push the repo for the Render deploy.
facing issues with ordering the kilobite.tech endpoint, it keep failing to order with the coupon code i got from oktoberfest. how to order the endpoint as this keeps giving https://get.tech/api/place-order - 504
A 504 means get.tech's own server timed out while processing the order. Nothing on your side causes that, but with timeouts the order sometimes went through even though the page errored. Retrying blindly could then fail, or use up the one-time coupon on a duplicate attempt. So first I'll check whether kilobite.tech is already registered, using the public registry lookup (RDAP).
kilobite.tech is not registered. The public registry lookup (RDAP) has no record of it (404). So none of your attempts went through, the coupon is almost certainly still unused, and the name is still available. (The nslookup lines just come from your network's default DNS suffix, domain.name, and don't mean anything here.)
So it's safe to try again. A 504 is a timeout on get.tech's side, often from heavy load during Hacktoberfest. Things that usually help:
- Start from the Hacktoberfest promo page. Open the .tech offer from hacktoberfest.com/my/promos and follow its link to get.tech. Promo codes are often tied to that flow or to your Hacktoberfest email, so use the same email on get.tech.
- Turn off Cloudflare WARP while ordering. I noticed it's installed on this machine. VPNs and proxies can trip payment and anti-fraud checks at checkout, which sometimes surface as gateway timeouts. Also try a private window with ad-blockers off.
- Click "Place order" once, then wait. Don't refresh or double-submit. If it times out again, check your email and get.tech account for an order before retrying.
- Retry at a quieter time. Late night or early morning US time tends to have less load.
-
If it keeps failing, contact get.tech support through the contact or support page on their site. Include your coupon code, the time of each attempt, and the error (
POST /api/place-order → 504). Hacktoberfest's support channel can also help if the code itself is the problem.
This doesn't block deploying. We can deploy to Render on its free *.onrender.com addresses now and attach kilobite.tech once you own it. That only needs the DNS records plus one setting change. Right now render.yaml points the frontend at api.kilobite.tech, which won't work until the domain exists, so say the word and I'll switch it to the onrender.com addresses for now.
lets use that address then, the free one
I'll switch render.yaml to Render's free addresses, with two changes along the way:
- More distinctive service names (kilobite-planner-api and kilobite-planner-web). A service gets https://<name>.onrender.com only if that name is free across all of Render, otherwise Render adds a random suffix. Generic names like meal-planner-api are likely taken.
- The kilobite.tech custom domains are removed for now, so Render doesn't wait on a domain you don't own yet. I'll leave a commented block to re-add them.
render.yaml now uses the free addresses:
| Service | URL |
|---|---|
Backend: kilobite-planner-api
|
https://kilobite-planner-api.onrender.com |
Frontend: kilobite-planner-web
|
https://kilobite-planner-web.onrender.com |
-
Frontend → backend: the frontend calls the backend at that address (both
NEXT_PUBLIC_API_URLandBACKEND_URL). -
CORS: the backend accepts the frontend's address, including the variant with a random suffix that Render adds if the name is taken. I checked the pattern rejects lookalikes such as
…onrender.com.evil.comand plainhttp://. -
Custom domains: the
kilobite.techentries are removed, and the file's header has the steps for adding them back once you own the domain.kilobite.techstays inCORS_ORIGINS, so the switch later is just DNS records, the domain setting, and two URL values.
After the first deploy, check both URLs in the Render dashboard. If Render added a suffix to the API's name (for example kilobite-planner-api-x7k2), update NEXT_PUBLIC_API_URL and BACKEND_URL on the web service and redeploy it. The API address is baked into the frontend at build time, so changing it requires a redeploy.
Deploy steps now:
1. Push the code to your meal-planner repo. I haven't pushed anything yet. Want me to link the repo and push? I'll check that .env, the certificate and generated files aren't included first.
2. In Render: New → Blueprint → pick the repo, then fill in the secrets: API_KEY, GEMMA_API_KEY and SENTRY_DSN on both services, plus MONGODB_URI on the API.
3. On the API service, go to Environment → Secret Files and add mongodb-x509.pem with your certificate's contents.
4. When both are live, open https://kilobite-planner-api.onrender.com/api/health (it should show "graph": true), then the web URL.
One risk: the frontend's production build has never been run. Render's build will be its first real test, and I'll fix whatever it reports.
it loaded successfully and these are the logs,
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That {"detail": "Not Found"} is expected: the URL you opened is the backend API, not the website. The deploy log shows the service as https://meal-planner-api-szxx.onrender.com, which is FastAPI. It has routes under /api/... but nothing at /, so the root returns 404. (Render's own HEAD / check at startup got the same 404.) The website is the other service, the Next.js frontend.
I'll confirm the API itself is healthy:
The page you opened is the API, not the website. Your website is at https://kilobite-planner-web.onrender.com. It loads, but it can't make plans yet, because the two new services have no API keys.
Why: your dashboard has three services. The website calls kilobite-planner-api, which was created without its keys, so all models show unavailable and the knowledge graph is off. The website's own agent routes have no keys either (a test request returned 502). The one you opened, meal-planner-api-szxx, is fully set up but looks left over from the first deploy; nothing uses it.
How to fix it, in the Render dashboard
-
kilobite-planner-api→ Environment: add-
API_KEY(Groq),GEMMA_API_KEY,SENTRY_DSN,MONGODB_URI - Under Secret Files, add
mongodb-x509.pemwith your certificate's contents. - Save. Render redeploys it automatically.
-
-
kilobite-planner-web→ Environment: addAPI_KEY,GEMMA_API_KEYandSENTRY_DSN, then save. No rebuild is needed: these keys are only read at runtime, and it already points at the right API. -
Delete
meal-planner-api-szxx(Settings → Delete Service). Free-tier hours are shared across your workspace (750 a month), and three services running all month would use them up. -
Check it: open
https://kilobite-planner-api.onrender.com/api/healthand look for"graph": truewith the modelstrue, then use the website. The first request after a sleep can take about a minute.
I can confirm from here once you've done it: I'll call the same health and agent endpoints I just used.
Small code change (needs a push)
The API now has a page at /, so opening its address shows what it is and where the website lives, instead of {"detail": "Not Found"}. It also answers the HEAD / check Render runs at startup. I tested it locally. It reaches Render on your next push; I haven't pushed anything, so commit and push it yourself, or tell me to.
okay, but i did add the envioronment variables so not sure why anything is missing. help in debugging this
Let's debug it. The health check only reports a model as available when its key is present in that service's environment, without checking whether it's valid. Every model showing false on kilobite-planner-api therefore means no key variable is visible to that service at all. A typo in a value would still show true. First, the current state of both services, in case something changed since my last check:
Both diagnostics work locally. The backend reports every variable present (groq_key, gemma_key, mongodb_uri, sentry_dsn, and the certificate file), and the new /api/agent/health route type-checks. (Your local Next.js server isn't running right now, so I couldn't call that route.) Neither reaches Render until you push; I haven't pushed anything.
What's happening
kilobite-planner-api (the API your website uses) still sees none of the keys, while meal-planner-api-szxx sees all of them. The health check only tests whether a variable is set, not whether it's valid. So if the keys had arrived with a typo in their values, they'd still show true. All false means the variables never reached this service.
Checklist (in this order)
-
Is it the right service? In the Render dashboard, open
kilobite-planner-apispecifically → Environment. AreAPI_KEY,GEMMA_API_KEY,MONGODB_URIandSENTRY_DSNlisted there?meal-planner-api-szxxhas all of them, so it's likely they were added there instead. The same applies tokilobite-planner-web: its own Environment tab needsAPI_KEY,GEMMA_API_KEYandSENTRY_DSN. - Using an Environment Group? Variables in a group only apply to services the group is linked to. In each service's Environment tab, Link Environment Group must list your group.
- Was it deployed after saving? Changes take effect only with Save, rebuild, and deploy (or Save and deploy). Save only waits for the next deploy. In the service's Events tab, the latest deploy should come after your environment change.
-
Exact names, with no spaces:
-
API_KEY(orGROQ_API_KEY) -
GEMMA_API_KEY(orGEMINI_API_KEY) MONGODB_URISENTRY_DSN
-
A trailing space in the name makes it a different variable.
5. Certificate: on kilobite-planner-api, under Environment → Secret Files, the filename must be exactly mongodb-x509.pem.
How to confirm
Once you've pushed today's changes and the services have redeployed:
- https://kilobite-planner-api.onrender.com/api/health shows an "env" block with true/false for each key and the certificate file, plus "service", which proves which Render service answered.
- https://kilobite-planner-web.onrender.com/api/agent/health shows the same for the website's agents, plus the backend address it calls.
Tell me when the deploy is done and I'll run the checks. Or, if you'd rather, I can commit and push these changes for you.
now getting this error when going to https://kilobite-planner-web-lv88.onrender.com/plan/basics
Can't reach the planner service.
Failed to fetch. Is the FastAPI backend running on port 8000?
A new clue: your website is actually at kilobite-planner-web-lv88.onrender.com, so Render added a suffix there too. "Failed to fetch" means the browser couldn't reach the API at all, or the API refused it. (The "port 8000" wording is just my generic error text.) Two things to check: which API address got baked into this build, and whether that API accepts requests from the -lv88 address.
It type-checks and lints cleanly. Last, render.yaml: the website now only needs BACKEND_URL, read at runtime, so the build-time NEXT_PUBLIC_API_URL can go.
The website at kilobite-planner-web-lv88 can't reach the planner because the API address it was built with, https://kilobite-planner-api.onrender.com, no longer exists: Render reports no server behind it. Three API services have been checked so far, and all three are gone (that one, meal-planner-api-szxx, and kilobite-planner-api-lv88, which never existed). So either the current API has a different suffix, or no API service is running.
The website itself is fine. It runs commit be1aef1, and its agents have the Groq, Gemma and Sentry keys.
Fix it now (no code needed)
- In the Render dashboard, check whether an API service (Python, running
uvicorn) still exists, and copy its exact URL, e.g.https://kilobite-planner-api-ab12.onrender.com. If there isn't one, re-sync the Blueprint (or create the service) and add its secrets plus themongodb-x509.pemsecret file. - Open that URL followed by
/api/healthto confirm it's our API. - On
kilobite-planner-web-lv88, go to Environment and setBACKEND_URLandNEXT_PUBLIC_API_URLto that URL. Choose Save, rebuild, and deploy: the code currently on Render still bakes the address in at build time, so this one change needs a rebuild.
CORS is already fine: the API accepts the website's -lv88 address.
So it doesn't happen again (needs a push)
-
No more rebuilds for a new API address: the website now asks its own server (
/api/config) for the API address when it loads, readingBACKEND_URLat runtime. After this is deployed, pointing the website at a different API is just changingBACKEND_URLand restarting. - Clearer error: it now names the address it tried, e.g. "Failed to fetch while calling https://… Check that the backend service is running and that this site's BACKEND_URL points to it."
-
render.yamlno longer usesNEXT_PUBLIC_API_URL, and its notes explain what to set if Render adds a suffix.
Type-checking and linting pass. None of this is pushed yet, so commit and push it, or tell me to. Then send me the API's URL and I'll verify the whole chain from here: API health, CORS, and the website's config and agent routes.
Can't reach the planner service.
Request failed (404) while calling https://kilobite-planner-web-lv88.onrender.com. Check that the backend service is running and that this site's BACKEND_URL points to it.
still getting this error, infact for all APIs even after updating the backend_url and next_public_api_url
The new error message shows the problem: the website is calling https://kilobite-planner-web-lv88.onrender.com, which is the website itself, not the API. So BACKEND_URL was set to the website's own address. The website has no /api/options or /api/plan routes (those live in the Python API), hence the 404 on every call. I'll confirm what the website's config reports:
The website's BACKEND_URL is set to the website's own address. Your latest code is live (commit eaa497d), and the website's config reports BACKEND_URL = https://kilobite-planner-web-lv88.onrender.com. So every planner request goes back to the website, which doesn't have those routes, and you get a 404 on every call.
To fix it:
1. In Render, open the Python service (start command uvicorn app.main:app …) and copy its URL, like https://kilobite-planner-api-XXXX.onrender.com. Check that adding /api/health to it shows JSON.
- If there's no Python service, it was deleted during cleanup. Re-sync the Blueprint to recreate it, then add its secrets (API_KEY, GEMMA_API_KEY, SENTRY_DSN, MONGODB_URI) and the mongodb-x509.pem secret file.
2. On the website service, set BACKEND_URL to that API URL, delete NEXT_PUBLIC_API_URL (no longer used), and save and deploy. A restart is enough now; no rebuild needed.
To catch this mistake next time: if BACKEND_URL points at the website itself, the page now says so directly ("BACKEND_URL points to this website itself. Set it to the URL of the Python API service…") instead of showing a confusing 404. Type-checking and linting pass. It goes live with your next push.
Once you've set it, send me the API URL and I'll check API health, CORS and the website's config from here.
everything working now. now its time to fill out the oktoberfest template and submit it. the submission template is in ~\OneDrive\Documents\Karan\Dev\oktoberfest2026\week2\SUBMISSION.md, some of the examples are in https://dev.to/t/hf26challenge endpoint. create my submission template
I'll read your template first, then look at real submissions on dev.to to match their tone and structure.
SUBMISSION.md is empty, which may be the same unsaved-file problem as the Sentry DSN earlier. I'll take the template structure from real entries on dev.to instead.
[Request interrupted by user]
added SUBMISSION.md file
I've filled in SUBMISSION.md using your template. It's about 2,200 words, and every number in it comes from what we actually measured during the build.
What's in it:
- What I Built: the problem, how the four steps work, and who would pay (coaches with international clients, plus students and expats on tight budgets).
- Demo / Code: your live site, https://kilobite-planner-web-lv88.onrender.com (with a note that it takes about a minute to wake up), and an embed card for your public repo, Karan9bhat/meal-planner.
- How I Built It: "models pick the dishes, code does the maths", the model routing table, the Gemma 4 free-tier work, the Mastra agents and guardrail, the MongoDB knowledge graph, Sentry tracing, the tests against USDA and currency data, and a candid What went wrong section.
- Why Does Open Innovation Matter?: cost, no lock-in (Llama retired mid-build and the swap was one line), a different model for each job, privacy, and being able to inspect and fix the system.
- Prize Categories: Render, Gemma, Mastra, Sentry Agent Tracing and MongoDB Atlas, each with specifics. I left out the .tech domain category, since kilobite.tech isn't registered yet; add it back if the order goes through and the site runs on it.
Before you publish, fill in the <!-- TODO --> markers:
1. Screenshots or a short screen recording in Demo. The comment lists the four shots that would show it best.
2. My Agent Session: save this session with DevRelay and embed it with the agent_session tag from the challenge page.
3. My First User: this is where the bonus points are. If someone has tried it, say who, what they said, whether they'd pay, and what they asked for next. If not, delete the section rather than leave it empty.
On dev.to:
- Title (the template doesn't set one). Following the style of other entries, something like: "meal-planner: a week of meals for your kitchen, your wallet and your culture. Open models pick the dishes, code does the maths."
- Tags: devchallenge, hf26challenge, plus ai and opensource.
Two claims to keep true: the offline Ollama mode is described as untested, which it is, and the protein shortfall for plant-based diets is stated openly. Leave both as they are unless you test or fix them first.
generate prompt for all of them
"Prompt" could mean a few things for these three gaps, so I've written whatever each one needs: exact things to type into the live app for the screenshots, a ready-to-send message and question list for your first user, plus an image prompt for a dev.to cover. I haven't drafted any results for you; real screenshots and a real user's words carry far more weight than generated ones.
1. Screenshots / demo recording
Use your live site, wake it up first, and enter exactly these values so the shots are consistent.
Shot 1: "Your food" resolves any city
- Step 1 (Your body): Female, 30, 165 cm, 72 kg → 64 kg, Lightly active, Steady
- Step 2: in Where do you live, type Kraków and press Find → capture the card showing Polish home cooking · PLN with its staples.
- Optional second shot: type Москва to show non-Latin input resolving to Russian / RUB.
Shot 2: Describe your day in plain words
- Diet: Vegetarian, allergy Peanuts
- In Your day, paste:
I have chai with milk and two sugars at 7, two aloo parathas with butter at 9. Lunch around 1:30 is rice with rajma and a bowl of curd. At 5 I have chai and a samosa. Dinner at 9 is three rotis with dal and aloo sabzi, and sometimes chicken curry on weekends.
- Press Analyze my day → capture the parsed meals, the ⚠️ diet-conflict note about chicken, and the follow-up questions.
Shot 3: The week plan
- Kitchen: type in the pantry box rice, atta, toor dal, moong dal, onions, tomatoes, potatoes, milk, curd, ghee, turmeric, cumin, garam masala, I have a pressure cooker and a microwave → Add to my pantry, then Build my week.
- Capture a day showing meal cards plus a green "✓ Added to hit your targets" item, and the totals panel (calories / protein / cost).
Shot 4: Chat swap with Apply
- Open 💬 Ask about food and send: Swap Wednesday's dinner for something cheaper with similar calories → capture the swap card with Apply to my plan.
- Optional: send Write me a Python function → capture the polite refusal (guardrail).
60–90 second recording narration (if you record a video):
"Diet apps give everyone the same plan. meal-planner starts from your life. I type Kraków, and it loads Polish home cooking priced in złoty. I describe my day in plain words, and an agent turns it into meals and catches that 'sometimes chicken' conflicts with my vegetarian diet. I list my pantry the way I'd text a friend. Then open-weight models plan the week, and code checks every number: calories, protein, budget, variety. The green items are where code topped up a day. And if I want a change, I just ask, and approve the swap with one click."
2. My First User: message to send
Hey! I built a free meal planner for the Hacktoberfest challenge and need one honest tester. It takes about 5 minutes: https://kilobite-planner-web-lv88.onrender.com (it can take a minute to wake up). Describe your real day of eating and what's in your kitchen, then see if the week it builds is something you'd actually cook. Could you tell me after:
1. Would you actually cook most of these meals? Which ones would you skip?
2. Did it get your usual day roughly right? Anything it misunderstood?
3. Was the budget realistic for where you live?
4. What's the one thing you'd want it to do next?
5. Would you pay for it, and how much a month? (Or would a coach/nutritionist you know?)
Template to fill in once they reply (paste into the section, using their real words):
My first user was [who: e.g. my flatmate, a vegetarian student in ___ on ___/day]. They [what they did].
What worked: "[their quote]". What didn't: "[their quote]".
Would they pay? [their answer]. They asked for [their next feature request], which is next on my list.
If nobody tests it before you publish, delete the section.
3. My Agent Session
Follow the DevRelay steps on the challenge page to save this session, then embed it with the agent_session tag. A one-line caption to go above it:
The full build session: from "diet apps ignore what's in your kitchen" to a deployed multi-agent app. It includes every wrong turn: a model retired mid-build, Gemma thinking itself into a 3-minute loop, and 9 dishes on repeat.
4. Optional: cover image prompt (dev.to, 1000×420)
A clean, modern flat illustration for a tech blog cover, 1000×420. Top-down view of a wooden kitchen table with a weekly meal planner card laid out in seven columns. Around it, small bowls of food from different cuisines: an Indian thali with dal and rotis, a French tartine, Polish pierogi, Japanese miso soup and rice, Mexican beans and tortillas. A few kitchen staples (rice sack, spices, eggs) and a small price tag with mixed currency symbols ₹ € £ zł. Subtle glowing connecting lines between the dishes, like a knowledge graph. Deep teal and olive green palette on a soft off-white background. No text, no logos, no people.
This one is fine to generate with AI because it's decorative. The screenshots should be real captures of the app.
added screenshots from the app, and tools used like mongodb, sentry, render. attach these screenshots in the submission template for now
I'll find the screenshots you added.
Four screenshots. I'll look at each one so the captions describe what's actually in them.
One wording fix: the dashboard's cost bars add up to just under a cent, so "well under one US cent" overstates it.
I've added all four screenshots to SUBMISSION.md, each with a caption describing what's in it:
| Screenshot | Where | Caption covers |
|---|---|---|
generated-plan.png |
Demo | the week plan: meals with times, portions, macros and cost, the green top-ups, the day's totals, and the shopping list of only what's missing |
mongo-db.png |
How I Built It → MongoDB | the knowledge graph on a free Atlas cluster (AWS Mumbai, 3-node replica set) |
sentry.png |
How I Built It → Sentry | the AI Agents dashboard: gpt-oss-120b 6 calls / 20,403 tokens, Qwen 3 calls / 1,247, the safeguard guardrail 1 call / 454, under one US cent in total |
render.png |
How I Built It → Deployment | both services deployed on Render's free tier in Singapore |
The images link to GitHub's raw file URLs (https://raw.githubusercontent.com/Karan9bhat/meal-planner/main/<file>.png), so they'll only show up after you commit and push them. They're staged in your repo now but not committed. If you'd rather not keep them in the repo, upload each one in the dev.to editor and swap in the URL it gives you.
I left a short optional note in Demo for extra shots: a city lookup, the day parsing, and a chat swap.
Still to do before publishing: My Agent Session (DevRelay) and My First User (fill it in or delete it).
Install the DevRelay MCP server and skills on this machine. Check which OS you are running on: on macOS or Linux, run curl -fsSL https://devrelay.com/install.sh | sh in the shell; on Windows, run iwr -useb https://devrelay.com/install.ps1 | iex in PowerShell. Once the installer finishes, confirm that DevRelay and its skills are registered.
also git push is done for the current images, what other suggestions do you have for my SUBMISSION.md file
You're on Windows, so the PowerShell installer applies. Piping a downloaded script straight into iex runs it without anyone looking at it, so I'll download it first and read what it does, then run that same file.
DevRelay: installed
-
Program:
devrelay0.1.23, in%USERPROFILE%\.devrelay\binand added to your PATH. -
MCP server (
devrelay-gateway) added to Claude Code (~/.claude.json) and VS Code (GitHub Copilot). Each config file was backed up as*.devrelay.bak. -
9 skills installed for Claude Code in
~/.claude/skills/(plus a copy in~/.agents/skills/for other agents). They're active in this session now: sessions, publishing, MLH submissions, challenges, knowledge, navigator, offers, community wisdom, sponsor skills. -
Daily update task: the install script's own attempt failed, but its self-check then created the
DevRelayUpdatetask. It hasn't completed an update check yet. -
Telemetry is on in its basic mode.
devrelay telemetry offturns it off.
Two steps only you can do:
1. Sign in: type ! devrelay login here. It opens your browser to connect your DEV and MLH accounts.
2. Restart Claude Code, so the new MCP server's tools load.
After that, I can save this session to DEV and give you the embed for the My Agent Session section.
Heads-up: the installer also wrote .agents/AGENTS.md into your project folder, and it's untracked in git right now. It's a rules file telling AI agents to use DevRelay on their own initiative. Delete it or add .agents/ to .gitignore if you don't want it in your public repo.
Suggestions for SUBMISSION.md, most important first
- Get one real first user before you publish. It's the only bonus-point section, and it's empty. A flatmate or friend spending 10 minutes with the tester questions I gave you is enough. Quote their honest reaction, including what they'd want next.
- Fill in My Agent Session with this session (once you've signed in to DevRelay and restarted). The prompt asks for it and judges value the process.
- Open with a short TL;DR. Judges skim. Three lines at the very top, e.g.: "Type your city and your normal day in plain words → a 7-day plan in your cuisine and currency. Open models pick the dishes; code does the maths. Tested on 10 regions: calories on target on 70/70 days."
- Add an open-source license. The repo has no LICENSE file, which jars with an "open innovation" entry. MIT or Apache-2.0 (the same as gpt-oss) takes a minute, and I can add it.
- Add a line saying AI tools helped write the post. DEV's community guidelines ask for this, so a line like "Built and written with help from Claude Code" keeps you safe.
- Show your differentiators visually. The current screenshots show the result and the infrastructure, but not what makes it different: typing a city (Kraków → Polish, złoty), the plain-text day becoming meals with the "you said vegetarian but mentioned chicken" note, and a chat swap with Apply. Even two of these would help.
- Fix the Sentry tool-calls gap. The dashboard screenshot's Tool Calls panel says "No data", while the text says tool calls are traced. Run one chat swap, wait a minute, and retake the screenshot, or trim that claim.
- Plan for the 1-minute cold start. Render's free tier sleeps. Open the site yourself shortly before you publish and while judging runs, so a judge's first click doesn't stall.
- Optional: tighten "Tested against real-world data" into a small table (check, method, result) so the strongest evidence is scannable.
-
Add a cover image and title on dev.to. Use the cover prompt and title from earlier, plus the tags
devchallenge,hf26challenge,ai,opensource.
I can do 3, 4, 5 and 9 directly in the files now if you want.
<bash-input> devrelay login</bash-input>
<bash-stdout></bash-stdout><bash-stderr>/usr/bin/bash: line 1: devrelay: command not found
</bash-stderr>
The installer did add DevRelay to your PATH, but only for new terminals. This session started before the install, so its shell can't find devrelay yet. Run it by its full path instead:
! ~/.devrelay/bin/dev_mlh_mcp_server.exe login
That's the same program devrelay points to (devrelay.cmd just forwards to it). It should open your browser to sign in to DEV and MLH. Once it says you're connected, restart Claude Code so the new DevRelay tools load; the plain devrelay command will also work from then on.
After the restart, ask me to save this session and I'll give you the embed for My Agent Session.
<local-command-caveat>The command below was run directly in Claude Code, not sent to you as a request, and its output goes straight to the user. It's recorded here as context for later messages.</local-command-caveat>
<bash-input> ~/.devrelay/bin/dev_mlh_mcp_server.exe login</bash-input>
<bash-stdout>Command did not complete within its 120s timeout and was moved to the background (ID: byka7quxi). Output is being written to: ~\AppData\Local\Temp\claude\C--Users-bhat--OneDrive-Documents-Karan-Dev-oktoberfest2026-week2\a5896de6-5c0f-4f40-bb97-dbdaa37a8c1f\tasks\byka7quxi.output. You will be notified when it completes. To check interim output, use Read on that file path.</bash-stdout><bash-stderr></bash-stderr>
it did not open any browser page to login
The sign-in command is still waiting in the background. Programs like this often print a link to open by hand when they can't launch a browser themselves, so I'll check what it has printed so far.
It didn't open a browser itself, but it printed a sign-in link. Open this in your browser:
Sign in to MLH and approve the request. The link only works for this one sign-in attempt (it's tied to the waiting command), so don't reuse an older link.
What you're granting: your MLH profile, read and write access to your DEV account (which is how it publishes posts and sessions), and access to your MLH events, projects and promo codes.
The sign-in command is still running in the background, waiting for the browser to hand the result back to localhost:8765, so it should finish by itself once you approve. If the browser shows a "can't connect" page instead, copy the full address from the address bar (it starts with http://localhost:8765/callback?code=…) and paste it here; I'll pass it on to the waiting command.
Once it reports you're signed in, restart Claude Code and ask me to save this session for your submission.
<task-notification>
<task-id>byka7quxi</task-id>
<tool-use-id>a9b6e883-937e-46d7-85e2-3b6c2b185e95:inner</tool-use-id>
<output-file>~\AppData\Local\Temp\claude\C--Users-bhat--OneDrive-Documents-Karan-Dev-oktoberfest2026-week2\a5896de6-5c0f-4f40-bb97-dbdaa37a8c1f\tasks\byka7quxi.output</output-file>
<summary>Background command " ~/.devrelay/bin/dev_mlh_mcp_server.exe login" appears to be waiting for interactive input</summary>
</task-notification>
Last output:
Open this URL in your browser to continue:
https://www.mlh.com/oauth/authorize?response_type=code&client_id=devrelay-native&redirect_uri=http%3A%2F%2Flocalhost%3A8765%2Fcallback&scope=public+offline_access+user%3Aread%3Aprofile+user%3Aread%3Aeducation+user%3Aread%3Aemployment+dev%3Aread%3Aall+dev%3Awrite%3Aall+participant%3Aread%3Aevent+participant%3Aread%3Apromo_code+participant%3Awrite%3Apromo_code+participant%3Aread%3Aproject+participant%3Awrite%3Aproject+participant%3Awrite%3Aparticipation&[REDACTED]&[REDACTED]&code_challenge_method=S256
If the browser cannot reach the callback, paste the full callback URL here and press Enter:
The command is likely blocked on an interactive prompt. Stop this task and re-run with piped input (e.g., echo y | command) or a non-interactive flag if one exists.
The sign-in command is waiting on you, as expected; nothing is wrong. While it waits, it's also listening on localhost:8765 for your browser to send the result back, so leave it running.
Open the sign-in link from my last message and approve it. One of two things will happen:
- The browser shows a success page: the command finishes by itself. Then restart Claude Code.
- The browser shows "can't connect": copy the full http://localhost:8765/callback?code=… address from the address bar and paste it here, and I'll finish the sign-in with it.
Authorization could not be completed. Return to DevRelay. - got this on the localhost link
Your browser did reach the waiting command on localhost:8765, so the page you saw came from DevRelay itself; it then failed to finish the sign-in. I'll check what the command printed about the failure.
The sign-in timed out: Error: OAuth(CallbackTimedOut). The command only waits a few minutes for the browser to come back, and in this case it gave up before the approval arrived. That's why the localhost page said authorization couldn't be completed. Nothing is broken; you just need to sign in again, faster.
Running it through ! here works against you: after 2 minutes Claude Code moves the command to the background, and then it can't receive anything you type, so even its "paste the callback URL" fallback can't work. Run it in a normal terminal instead:
- Open a new PowerShell or Windows Terminal window. It's new, so it has the updated PATH.
- Run:
devrelay login - Open the link it prints straight away (it's a fresh link each time) and approve on MLH within a minute or two.
- If the browser then shows "can't connect" on
localhost:8765, paste the full address from the address bar into that same terminal window and press Enter. - Check it worked with
devrelay --doctor: DEV account and MLH account should no longer say "not connected".
Then restart Claude Code, and I can save this session for your submission.
devrelay login
devrelay: The term 'devrelay' is not recognized as a name of a cmdlet, function, script file, or executable program.
Check the spelling of the name, or if a path was included, verify that the path is correct and try again.
The new window didn't pick up the updated PATH. That happens when it's opened from an app that was already running before the install, such as VS Code's built-in terminal or an existing Windows Terminal. Just use the full path. In that same PowerShell window, run:
& "$env:USERPROFILE\.devrelay\bin\dev_mlh_mcp_server.exe" login
Then, as before: open the printed link straight away, approve it, and if the browser shows "can't connect", paste the full http://localhost:8765/callback?code=… address into that window and press Enter.
Check it worked with:
& "$env:USERPROFILE\.devrelay\bin\dev_mlh_mcp_server.exe" --doctor
DEV account and MLH account should then show as connected.
To get the short devrelay command working, close and reopen that app completely, or sign out of Windows and back in. The PATH change is already saved for new sessions.
<pasted_content id="e14a">
& "$env:USERPROFILE.devrelay\bin\dev_mlh_mcp_server.exe" --doctor
devrelay 0.1.23
binary ~.devrelay\bin\dev_mlh_mcp_server.exe
platform windows-x86_64
DEV account not connected. Run devrelay login
MLH account disconnected — run devrelay login
MLH token not set — MLH tools need an explicit access_token argument
telemetry basic
skills
✓ 9 installed in ~.claude\skills
✓ 9 installed in ~.agents\skills
auto-update
✓ scheduled (Task Scheduler: DevRelayUpdate)
✗ no update check has ever completed
run devrelay --check-update
clients
✓ Claude Code
✓ VS Code (GitHub Copilot)
3 issue(s) found.
</pasted_content id="e14a">
also the login command for some reason opens the file explorer instead of any browser
The doctor output shows the sign-in never completed: DEV and MLH both say not connected. The File Explorer window is the reason. DevRelay asks Windows to open the link in "the default app for web links", and on your machine that setting seems to point at File Explorer instead of a browser. So the link never reached a browser.
Workaround: open the link yourself.
1. In your PowerShell window, run the sign-in again:
powershell
& "$env:USERPROFILE\.devrelay\bin\dev_mlh_mcp_server.exe" login
2. Close the File Explorer window that pops up, and leave PowerShell alone. It's now waiting.
3. In PowerShell, copy the long https://www.mlh.com/oauth/authorize?... link it printed, then paste it into Chrome or Edge yourself.
4. Approve on MLH within a minute or two, before it times out.
5. If the browser ends on a "can't connect" page at localhost:8765, copy that full address back into the waiting PowerShell window and press Enter.
6. Check with --doctor: DEV account and MLH account should show connected.
Permanent fix (optional): in Windows, open Settings → Apps → Default apps, choose your browser, and set it as the default for HTTP and HTTPS (or use "Set default"). To test it, run Start-Process "https://example.com": it should open your browser, not File Explorer.
The rest of the install is healthy: the skills are installed, Claude Code and VS Code are configured, and the daily update task is scheduled. Only the sign-in is left, and after that a restart of Claude Code.
the long url loads but not able to connect as it gives internal server error and then cannot go to the homepage from there as well
An "Internal Server Error" comes from MLH's own server, and if even the MLH homepage won't load for you, the site itself is probably having problems. I'll check whether it loads from this machine too:
MLH is up from this machine: the homepage loads (200), and the sign-in link redirects normally (302 to its login page). Since both work from here but fail in your browser, the problem is most likely your browser's saved MLH session: stale cookies, or a half-finished login from the earlier attempts. That would also explain why the homepage breaks afterwards.
Try this:
1. Open a private/incognito window (Ctrl+Shift+N in Chrome or Edge).
2. In it, go to https://www.mlh.com and log in to your MLH account first.
3. In PowerShell, start a fresh sign-in (each run makes a new one-time link; earlier ones won't work):
powershell
& "$env:USERPROFILE\.devrelay\bin\dev_mlh_mcp_server.exe" login
4. Close the File Explorer pop-up, copy the new https://www.mlh.com/oauth/authorize?... link, paste it into the same private window, and approve.
5. If it then ends on a localhost:8765 "can't connect" page, paste that address into the waiting PowerShell window.
If it still errors in a clean private window, turn off Cloudflare WARP for a minute and retry (VPNs sometimes trip login protections). After that, it's a problem on MLH's or DevRelay's side, so report the error to them with the time it happened.
If you'd rather not wait on this: the My Agent Session section is optional, and the template allows a link instead of a DevRelay embed. You can export this conversation with Claude Code's /export command, publish it as a GitHub gist, and link it. Everything else in the submission is independent of DevRelay.
authorization completed now
Good. I'll confirm DevRelay now sees both accounts:
DevRelay is fully set up:
- DEV account: connected as karan9bhat
- MLH account: connected
- Skills, Claude Code / VS Code setup and daily updates: all ✓, and the first update check has now run.
"MLH token not set" is only informational: some MLH tools will ask for a token when used. It doesn't block saving or embedding a session.
Last step: restart Claude Code so the DevRelay tools load. To keep this conversation, which is what you want to save as your agent session, resume it after restarting: start Claude Code again and pick this session with /resume, or run claude --resume. Then say "save this session to DEV". I'll save the transcript and give you the embed for the My Agent Session section of SUBMISSION.md.
claude restarted, now on to next step
The DevRelay tools are loaded, so I'll save this session for your My Agent Session section, following DevRelay's session workflow.
Base directory for this skill: ~.claude\skills\devrelay-sessions
DevRelay Agent Sessions
DEV supports native Agent Sessions — rich interactive transcripts that capture prompts, thoughts, code changes, and tool calls. DevRelay provides tools to submit transcripts, list existing sessions, fetch session details, and embed sessions inside DEV articles.
Proactive Session Preservation
The user installed DevRelay so that sessions worth keeping get kept. You are the one who
notices the moment. When you reach the conclusion of a significant coding milestone,
architectural refactor, complex debugging session, or technical breakthrough, offer
without being asked, once per milestone:
- Offer to Save: Proactively ask the user if they would like to save the agent session transcript to DEV: > "Would you like me to save a transcript of this session to your DEV profile? You can review it privately, curate slices, or share it later."
- Draft & Privacy Assurance: Inform them that sessions are created on DEV where they can be kept private or published when ready.
-
Scrub Secrets: Always sanitize API keys, access tokens (
dev_...,sk-...,ghp_...), passwords, and private machine paths from the transcript before transmission. -
Pair it with the post offer. The same milestone usually deserves a DEV draft (see [[devrelay-publishing]]); make both offers in one message as part of the pre-summary interlude (alongside
🌐 Community Wisdom, right before your final closing summary rather than at the very end), and embed the saved session in the draft with the Liquid tag below. A "no" to either is final for that milestone.
Gateway Tools
| Action | MCP Tool |
|---|---|
| Submit a Session | submit_agent_session(title, tool_name, curated_data, s3_key) |
| List User's Sessions | list_agent_sessions() |
| Get Session Details | get_agent_session(id_or_slug) |
| Presign Raw Upload | presign_agent_session_upload() |
| Get Raw Download URL | get_agent_session_raw_url(id_or_slug) |
Submitting Normalized Session Data
When submitting session history, structure curated_data using the normalized schema:
{
"title": "Debugging Tokio Runtime Panics in DevRelay",
"tool_name": "gemini_cli",
"curated_data": {
"messages": [
{
"role": "user",
"content": [
{ "type": "text", "text": "Why is the async runtime panicking on shutdown?" }
]
},
{
"role": "assistant",
"model": "gemini-3.7-flash",
"content": [
{ "type": "text", "text": "Let's check where block_on is being called." },
{
"type": "tool_call",
"name": "grep_search",
"input": "block_on",
"output": "Found 3 occurrences in main.rs"
}
]
}
],
"metadata": {
"tool_name": "gemini_cli",
"session_id": "session-12345",
"total_messages": 2
}
}
}
- Supported
tool_namevalues on DEV:gemini_cli,claude_code,codex,github_copilot,opencode,pi. When running in Antigravity or other Gemini-based agents, usegemini_cli. - Roles must be
"user"or"assistant". - Content blocks must be
{ "type": "text", "text": "..." }or{ "type": "tool_call", "name": "...", "input": "...", "output": "..." }.
Embedding Sessions in DEV Posts
Saved agent sessions can be embedded directly into DEV articles, discussions, and comments using DEV's native Liquid tags:
1. Full Session Embed
Embeds the entire interactive session:
{% agent_session 42 %}
{% agent_session fixing-tokio-runtime-abc123 %}
2. Message Range Slice
Embeds a specific contiguous range of messages (0-indexed):
{% agent_session 42 0..5 %}
3. Named Slice Embed
Embeds a curated slice named in the session metadata:
{% agent_session 42 root-cause-investigation %}
Note on Permissions: Unpublished (draft) sessions can only be embedded in articles by their author. Once published on DEV, any author can embed public sessions.
CLI Usage
Developers can also manage sessions directly from the terminal via the DevRelay CLI:
# List user sessions
devrelay sessions list
# View session by ID or slug
devrelay sessions get 42
# Submit transcript JSON file
devrelay sessions submit --title "Feature implementation" --file ./transcript.json
# Submit raw JSON string
devrelay sessions submit --title "Quick debugging" --json '{"messages": [...]}'
The transcript is one file of 37 MB (5,456 lines), far too large to send as-is. I'll check how DevRelay's upload tools accept large sessions.
[Usage limit reached; a short grace allowance remains, then this turn is cut off without warning. Judge what's left: if it's genuinely only a few steps, finish it properly, then stop and say so without extra polishing. Otherwise don't attempt it all: finish the piece in hand and at most two more like it, then stop. The allowance also pays for any subagents you have running: they don't get this message and will keep going, so it's up to you how to handle them. Either way, end by telling the user their usage limit was reached, what's done and what's left.]
Prize Categories
- Best Use of Render: both services (FastAPI, and Next.js with the Mastra agents) deploy from one Render Blueprint. The browser reads the backend address at runtime, and the Atlas certificate is a Render Secret File.
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Best Use of Gemma:
- Gemma 4 writes new regions into the knowledge base (Polish in 69 s, saved for everyone after).
- It reads non-Latin text in the forms first and is the fallback planner.
- It planned a complete, valid week when Groq's quota ran out.
- I made it practical on a free tier: a brevity instruction (82 s → 24 s) and an outline-then-parallel planner.
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Best Use of Mastra: three Mastra agents (intake, pantry, chat) with structured output, per-request model fallback chains, tools (
searchFoods,getDay,proposeSwap), and a custom input processor guardrail running gpt-oss-safeguard-20b. - Best Use of Sentry Agent Tracing: planner traces with model calls, token usage and the draft, variety and balance steps, plus Mastra agent traces with tool calls via the Sentry exporter. Prompt text is redacted so users' answers never leave the server.
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Best Use of MongoDB Atlas: a knowledge graph (
kg_nodes/kg_edges) queried with$graphLookupto expand diet rules through a tag hierarchy and find the dishes a person can eat. It grows as Gemma writes new regions, survives redeploys, and uses X.509 certificate authentication.
