This is a submission for the Hacktoberfest Open-Source AI Challenge: Touch Grass

What I Built

In Lagos the weather app on your phone says "60% rain" for the whole day, and then it shines until 4pm and pours at 5. So you either stay in all day, or you go out and get soaked.

Go Out Today? is a morning brief that answers one question: when is the best time today to be outside for the thing I want to do? You pick an activity (walk, run, football, market, drying clothes, commute, picnic, hangout), press one big play button, and a voice tells you something like:

"The best time for a picnic is between 3 pm and 6 pm with at most 29% chance of rain. Outside this window, rain chances peak at 54% around 12 pm."

Then you close the laptop and go. That is the whole idea for "Touch Grass": the screen should be the shortest part. At 05:45 a scheduled job builds the brief, so at 6am it plays straight away.

How it works:

TabPFN looks at three years of hourly Lagos weather and forecasts the rain chance and temperature for each of the next 12 hours.
Code works out the facts: the best window, the window to avoid, any heat.
A local Qwen model through Ollama turns those facts into two or three plain sentences.
ElevenLabs reads it out loud, with captions that light up word by word.

TabPFN and the model run on my laptop: an Intel UHD 620, 16 GB of RAM, no GPU.

It's for anyone who wants to get off the screen and go touch grass today, whether that's a walk, a run, a picnic, a hangout with friends or football with the guys. You don't need to read a weather chart or guess what "60% rain" means. Press play, listen for a few seconds, and it tells you the best time to go out and the time to avoid. Then you close the laptop and go.

I built it for me first. I'm the one who stays in all day because the app said 60%.

Demo

The cold-start part of the video is sped up (it says so on screen). The voice parts are real time, sound on.

The sky follows the real time of day: orange at sunrise, blue in the day, dark with a moon and stars at night. The character on the grass changes with the activity, so dry clothes gets a clothesline and picnic gets a blanket.

Taking it outside

Saturday 10 October. A friend was celebrating his birthday, so I had a party to get to. In the morning I picked Hangout and pressed play:

"The best time to hang out today is from 4 pm to 6 pm, with at most a 29% chance of rain."

It also said there were no windows to avoid, so the rest of the day wasn't a write-off either. I went to the party, and the 4–6 pm window held: no rain. I filmed the brief on my laptop outside, then pressed "I went outside ✓", typed one line and logged it. That log stays in a file on my laptop. It's the only "score" the app keeps: how often I actually went.

Here it is in real use: four activities I filmed on my phone, ending with logging the outing.

One good day isn't proof. Sep 30 (below) was a bad day for it. But on Saturday, it was the reason I left the house in the right two hours instead of guessing.

What my colleague said

Nobody else has used it yet, but I showed it to my colleague at work. She liked the idea of hearing the day before you've even properly got out of bed: you know the forecast and when you can go out, without opening an app.

Then she found the real feature. If you don't feel like going out and Go Out Today? puts the rain right in the window you were planning to go, that's a perfect excuse. I hadn't designed for that, but it's honest: the brief works in both directions.

Code

G00dS0ul-Hackerton / w1-go-out-today

A local, voice-first morning brief that tells people in Lagos the best time today to be outside.

w1-go-out-today

A local, voice-first morning brief that tells people in Lagos the best time today to be outside.

Quick Start

1. Prerequisites

Python 3.12 managed with uv
Ollama running locally with qwen2.5-coder:7b:
```
ollama pull qwen2.5-coder:7b
```

2. Setup

Clone the repository and install dependencies:

git clone https://github.com/G00dS0ul-Hackerton/w1-go-out-today.git cd w1-go-out-today uv sync

Copy .env.example to .env and configure your settings:

cp .env.example .env

Key settings:

ELEVENLABS_API_KEY: Your ElevenLabs API key (required for audio; or pass --no-voice for text-only).
ELEVENLABS_VOICE_ID: Voice ID (default: JBFqnCBsd6RMkjVDRZzb - George).
ELEVENLABS_MODEL_ID: Model ID (default: eleven_flash_v2_5 - 0.5 credits/char on free tier).
TABPFN_TOKEN: Your TabPFN token (required on Windows to prevent browser authentication prompts).
OLLAMA_BASE_URL: Ollama API endpoint (default: http://localhost:11434).
OLLAMA_MODEL: Chosen Ollama model (default: qwen2.5-coder:7b).

(Note: 3 years of hourly historical Lagos weather data is pre-bundled in data/lagos_weather.csv).

3. Run the Morning Brief (CLI)

Generate a 12-hour…

View on GitHub

Python 3.12 with uv, FastAPI, and vanilla HTML/CSS/JS with no build step. There are 64 tests and they all run offline: TabPFN, Ollama and ElevenLabs are stubbed.

How I Built It

Here's the whole pipeline. Only fetching the latest weather and the voice need the internet.

 05:45 scheduled job (or you press play) │ ▼ Open-Meteo last 2 days of Lagos weather + 3-year CSV (online) │ ▼ TabPFN rain chance + temperature, next 12 hours (local, CPU) │ ▼ Code: the facts best window · window to avoid · heat hours (no AI) │ ▼ Qwen via Ollama facts → 2-3 plain sentences (local) │ ▼ Guard numbers must match the facts, no "will rain", │ no false "at most X%" ── fails twice? → plain template ▼ ElevenLabs voice + word timings for captions (online) │ ▼ Press play, listen, close the laptop, go outside

1. The data: three years of Lagos weather

I pulled hourly weather for Lagos from the Open-Meteo archive (CC BY 4.0): 26,304 rows from 2023-10-01 to 2026-09-30. It has temperature, humidity, dew point, rain, cloud cover, pressure and wind. For real mornings the app also fetches the last couple of days from Open-Meteo's forecast API, because the CSV stops at the end of September.

2. TabPFN: a forecast without training a model

TabPFN is a foundation model for tables. You give it rows, it gives you predictions, and you don't train anything. I used one classifier (will it rain at this hour?) and one regressor (temperature), with "hours ahead" as a feature so a single model covers all 12 hours.

To keep myself honest I held out the last 30 days (forecasts issued at 06:00, 12 hours ahead, 360 rows) and compared against two simple guesses:

Climatology: "what is normal for this month and hour"
Persistence: "the next hours will be like right now"

Model	Temp error (MAE)	Rain AUC (higher is better)	Rain Brier (lower is better)
TabPFN	0.71 °C	0.733	0.205
Climatology	0.75 °C	0.606	0.231
Persistence	1.61 °C	0.663	0.322
Gradient-boosted trees (same 3,000 rows)	0.69 °C	0.748	0.199
Linear / logistic regression (same rows)	0.85 °C	0.645	0.250

TabPFN beats the simple guesses on every number. Then, the night before posting, I added the test I should have run first: scikit-learn's gradient-boosted trees, default settings, trained on exactly the same 3,000 rows with the same features. The trees edged TabPFN on all three numbers. The gaps are small (0.02 °C, 0.015 AUC, 0.006 Brier) on only 30 mornings, so I'd call it a tie rather than a loss, but it's not the win I was hoping to write.

So why keep TabPFN? I didn't tune anything. No hyperparameters, no training loop: one fit, and it landed within a whisker of a strong tree model on the first go. It also hands me probabilities straight away, and the brief is built on "chance of rain", never "it will rain". With more time I'd backtest more months and try averaging both. TabPFN is not magic, though. An AUC of 0.733 means that when you pick one hour that rained and one that didn't, it gives the rainy hour the higher chance about 73% of the time. That's useful, but it's not a crystal ball.

Two things surprised me:

More data didn't help.

Training rows	Temp MAE	Rain AUC	Rain Brier	Time
1,000	0.627	0.685	0.222	28 s
3,000	0.712	0.733	0.205	97 s
5,000	0.691	0.728	0.207	189 s

5,000 rows took twice as long as 3,000 and scored the same. I kept 3,000. I also did not switch to 1,000 for temperature, because that would be tuning on my test set.

More estimators didn't help either. n_estimators=10 gave the same accuracy as 2 (Brier 0.203 vs 0.205, AUC 0.733 both) but took about 8 minutes instead of 3.6. So it's 2 everywhere.

Then I checked one real day. On Sep 30 it called a wet midday (72–81% from 10am to 2pm) that never came, and the only rain fell at 5pm, when it had said 23%. Its temperatures were off by 0.8 °C on average. That one day is why the brief always says "chance of rain" and never "it will rain".

What broke on Windows: my Windows username has an emoji in it (don't ask). That emoji broke how a TabPFN dependency loads OpenBLAS. Then TabPFN's first-run licence login crashed with WinError 10038, because it reads the keyboard in a way that only works on Mac and Linux. The fix was to put a TABPFN_TOKEN in .env and load it before TabPFN starts.

3. Ollama: the code decides, the model only talks

I tested two local models, qwen2.5:7b and qwen2.5-coder:7b, giving each the same real forecast 6 times. I checked every answer with a guard: numbers must come from the forecast, it must say "chance of rain" (never "will rain"), and it must be 2–3 sentences.

qwen2.5:7b passed 2 of 6. Most answers were one sentence, and it kept saying "below 32°C", which was just the heat threshold from my prompt.
qwen2.5-coder:7b passed 6 of 6.

But "passed" was hiding something. One coder answer said 5–6pm was "the coolest at 26.2°C". 7am was cooler (25.4°C). The number existed in the forecast, so my guard let it through, but the claim was false.

So I changed the design. The code now works out the facts (best window, window to avoid, heat hours) and the model only turns those facts into sentences. The guard checks that every number matches the facts, and that no range claim is wrong ("at most 29%" has to be true for every hour in that window). If the answer fails, it retries once and then falls back to a plain template. The model can still word things badly, but it can't make up the weather.

This is the guard every sentence goes through (trimmed):

def validate_plan_output(text: str, facts: dict) -> tuple[bool, str]: if "will rain" in text.lower(): return False, "Forbidden phrase 'will rain' detected." sentences = [s for s in re.split(r"(?<=[.!?])\s+", text.strip()) if s] if len(sentences) not in (2, 3): return False, f"Sentence count is {len(sentences)}, expected 2 to 3." # "at most 29%" can't be stretched to the whole forecast if the peak is higher  for m in re.finditer(r"at most\s+(?:a\s+)?(\d+)%", text.lower()): ... # every number in the text must come from the facts the code computed  valid_nums = extract_fact_numbers(facts) for num in re.findall(r"\b\d+(?:\.\d+)?\b", text): if float(num) not in valid_nums: return False, f"Number '{num}' does not appear in the facts block." return True, "Valid"

TabPFN predicts, the code decides, and the LLM talks.

4. ElevenLabs: a voice on the free plan

When I went to claim the Hacktoberfest ElevenLabs credits, the promo pool was empty. So I built for the free plan:

eleven_flash_v2_5: about half the credits per character, and fast.
Cache by SHA-256(text + voice + model): the same brief is never paid for twice. Files are written to .part first, so a broken download never gets cached.
Voice-friendly text: "4 pm", not "16:00", and about 40 words.
With-timestamps endpoint: word timings drive the live captions.
Every failure becomes one line, and the plan still shows as text.

My first live call used 149 characters. One fun bug: a bad API key came back as a 400, not a 401, and my error message printed every response header to the screen. Now it says "API key is invalid (check .env)" and nothing else.

5. The page: hiding a slow pipeline

On my laptop a cold start takes a few minutes (about 4 in my demo recording), because TabPFN fits two models on the CPU and Ollama has to load 4.7 GB into memory. A spinner for that long would feel broken, so:

Pre-build at 04:30 with Windows Task Scheduler, so the brief plays instantly in the morning.
The forecast runs once per morning. Switching activity only reruns the plan and the voice.
Real progress from the server (Server-Sent Events), one step at a time: reading the sky → TabPFN forecasting → writing your plan → recording.
A waveform ring around the play button, drawn on a <canvas> from the Web Audio AnalyserNode and limited to the speech range.

Two bugs I'm glad I caught before recording. First, I picked "Dry clothes" and heard the walk brief, because the cache wasn't keyed by activity and an older request answered late. Now there's a cache per activity, request IDs, and AbortController. Second, the captions came out as Thebesttimeforawalk... because inline-block spans eat spaces.

Working with agents

I built this in Antigravity with a rule that the agent writes a plan, waits for my APPROVE, builds, then stops at "awaiting confirmation". It never marks anything done; only I do. Each milestone has a plan file, a journal entry with the real numbers, and a saved agent session. Most of the good catches above came from me reading the output and pushing back on the plan, not from the first draft.

Things I got wrong first

My Windows username has an emoji in it. That broke OpenBLAS loading inside a TabPFN dependency. Don't put emoji in usernames.
The captions read Thebesttimeforawalk. inline-block spans ate every space.
I asked for Dry clothes and got the walk brief. An older request answered late and the cache wasn't keyed by activity.
A model said 5–6 pm was "the coolest at 26.2°C". 7 am was cooler. The number was real, the claim wasn't. That's why the code decides now.
A bad ElevenLabs key printed every response header onto the screen. Now it says "API key is invalid (check .env)".
I assumed TabPFN would beat trees. It didn't, quite (see the table).

What this is not

It's not a weather service. It's one city, one CSV, and a 12-hour forecast from 06:00.
An AUC of 0.733 helps you pick your hours. It doesn't promise a dry afternoon.
It knows rain and heat, not traffic, flooding or how loud your street gets.
The cold start takes minutes on my laptop, which is why the brief is pre-built at 05:45.

Why Does Open Innovation Matter?

It runs on my laptop. TabPFN and Qwen run locally with no GPU and no cloud bill. Only the voice goes online, and if there's no internet you still get the full forecast and plan as text.
My routine stays with me. Where I am, what I do and when I go out never leaves my machine. The only thing sent to ElevenLabs is the two or three sentences of the brief. The "I went outside" log is a local file.
I could open it up and change it. I swapped two models and benchmarked them in an afternoon, added a guard, and moved the reasoning out of the model into code. You can't do that with a closed weather API.
It's grounded in my city. TabPFN learns from three years of Lagos history in my own CSV, not a global average.

An honest note: TabPFN's code is Apache 2.0, but the 3.5 weights I used are under a non-commercial licence. That's fine for a personal morning brief, and the README links the licence.

My Agent Session

These are the sessions behind the parts of the build where the agent and I had the most back and forth:

TabPFN forecast and the Windows fixes (M3)

W1 M3 - TabPFN rain forecast for Lagos

You

<USER_REQUEST>
Okay i want to start the M3 in the build plan(@[[REDACTED]🤡\G00dS0ul-Hackerton\docs\challenge\w1\build-plan.md] ), can you roll out subagent in parallel so you don't run out of context in the research and use the 3.1 pro high

So basically i dont have a GPU so tabpfn runs locally on the CPU, so you use the cpu-only pytourch build via the uv's pytourch-cpu index in the pyproject.toml and with this i will reduce the training set at most 5k rows for my cpu limit, hold out the last 30 days for testing and split it by time not randomly and no future data in the input

The rain target is precipitation >= 0.1mm in the next 3hours (yes/no) also preditcs temeprature per hour and the baseline should be persitence and climatology (month x hours)
The metrics to use is the ROC AUCand brier score for rain, MAE for tempnot just plain accuracy
Measure and print training and prediction time and Name Tabpfn version and its licence in the Readme log in the local vs client choice as a decision
</USER_REQUEST>
<ADDITIONAL_METADATA>
The current local time is: 2026-10-06T16:19:19+01:00.

The user has mentioned some items in the form @[ITEM]. Here is extra information about the items that were mentioned by the user, in the order that they appear:

@[[REDACTED]🤡\G00dS0ul-Hackerton\docs\challenge\w1\build-plan.md] is a [File]:
[REDACTED]🤡\G00dS0ul-Hackerton\docs\challenge\w1\build-plan.md
</ADDITIONAL_METADATA>
<USER_SETTINGS_CHANGE>
The user changed setting Model Selection from None to Gemini 3.1 Pro (High). No need to comment on this change if the user doesn't ask about it. If reporting what model you are, please use a human readable name instead of the exact string.
</USER_SETTINGS_CHANGE>

Agent

Created At: 2026-10-06T16:19:32+01:00
Completed At: 2026-10-06T16:19:32+01:00
File Path: file:///c:[REDACTED]%F0%9F%A4%A1/G00dS0ul-Hackerton/memory/index.md
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The following code has been modified to include a line number before every line, in the format: <line_number>: <original_line>. Please note that any changes targeting the original code should remove the line number, colon, and leading space.
1: # Memory index
2:
3: 
4: 
5: 
6:
7: One line per memory entry. This file is read on every task.
8:
9: The full entry lives in the shard named in brackets. Open that shard when a line below touches the task you are doing. Do not act on an index line alone, and do not load the whole memory folder by default.
10:
11: Precedence, highest first: corrections, decisions, patterns.
12:
13: Rebuild after any write to memory/: python tools/build-memory-index.py
14:
15: ## Corrections - things I got wrong. Never soften these.
16:
17: 0 entries. Next free id: C-1. Shards: corrections.md
18:
19: ## Decisions - settled choices. Do not relitigate.
20:
21: 5 entries, D-1 to D-5. Next free id: D-6. Shards: decisions.md
22:
23: - D-1 Python is the stack for every week - Python 3.12 with uv, pytest, ruff and python-dotenv for all four challenge repos unl... [decisions.md]
24: - D-2 One new repo per challenge, in the G00dS0ul-Hackerton org - Each challenge gets its own new repo (w1-..., w2-..., w3-..., w4-...) under the G00d... [decisions.md]
25: - D-3 Week 1 model is open-weight and not Gemma - Week 1 uses an open-weight model through Ollama that is not Gemma (Qwen or Llama fam... [decisions.md]
26: - D-4 Week 1 specific model is Qwen 2.5 7B [decisions.md]
27: - D-5 Week 1 specific model deferred to M4 - The final choice between qwen2.5:7b and qwen2.5-coder:7b will be made during Mil... [decisions.md]
28:
29: ## Patterns - conventions and non-obvious discoveries.
30:
31: 3 entries, P-1 to P-3. Next free id: P-4. Shards: patterns.md
32:
33: - P-1 Only Ise marks work done - Never set a task, milestone or plan to Done in any file, tracker or summary. You may... [patterns.md]
34: - P-2 Antigravity session upload to DEV goes through DevRelay curated submit - To save an Antigravity session to DEV, read the transcript at %USERPROFILE%.gemini... [patterns.md]
35: - P-3 Windows paths contain an emoji username - Never type my Windows home path by hand. Use %USERPROFILE% or $env:USERPROFILE, or t... [patterns.md]
36:
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Created At: 2026-10-06T16:19:32+01:00
Completed At: 2026-10-06T16:19:32+01:00
File Path: file:///c:[REDACTED]%F0%9F%A4%A1/G00dS0ul-Hackerton/docs/challenge/w1/build-plan.md
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1: # Week 1 build plan - Go Out Today?
2:
3: Repo: w1-go-out-today in the G00dS0ul-Hackerton org (public, MIT)
4: Window: 2026-10-05 to 2026-10-11. Target submit: 2026-10-11 10:00 Lagos.
5:
6: These are the tasks I am giving you, in order. For each one: plan it in
7: plans/, wait for my APPROVE, build it, then stop at
8: IMPLEMENTED - AWAITING ISE CONFIRMATION. Quote the acceptance criteria
9: below verbatim in the plan's scope check.
10:
11: ## Pipeline
12:
13: weather CSV -> TabPFN (rain risk + temperature per hour) -> Ollama model
14: (plan text) -> ElevenLabs (audio) -> person goes outside
15:
16: ---
17:
18: ## M1 - Set up the repo (Mon 2026-10-05)
19:
20: Set up w1-go-out-today as a uv Python 3.12 project with pytest,
21: ruff, python-dotenv, a .env.example, a .gitignore, an MIT LICENSE
22: and a README that only describes what exists. Recommend which Ollama model to
23: use (open-weight, not Gemma, runs on my laptop) with your reasons, and let me
24: choose.
25:
26: Acceptance criteria:
27: - uv run pytest passes with at least one smoke test.
28: - uv run ruff check reports no errors.
29: - .env is listed in .gitignore and git status does not show it.
30: - .env.example lists every key name with an empty value.
31: - The README describes only what exists, and has an empty
32: "Changes after submission" section.
33: - The chosen Ollama model is recorded as a decision in memory.
34:
35: ## M2 - Lagos weather data (Tue 2026-10-06)
36:
37: Write a script that downloads historical hourly weather for Lagos and saves
38: it as a CSV in the repo. Prefer a free source that needs no API key.
39: Recommend the source; I approve it in the plan.
40:
41: Acceptance criteria:
42: - One command (documented in the README) produces the CSV from scratch.
43: - The CSV covers at least 12 months of hourly rows for Lagos.
44: - Every column the forecast uses has no missing values, or the script states
45: how gaps are handled.
46: - The source and its licence/terms are named in the README.
47: - A test runs the cleaning step on a small fixture file, with no network.
48:
49: ## M3 - TabPFN forecast (Tue-Wed 2026-10-06 to 2026-10-07)
50:
51: Use TabPFN to predict, for each of the next N hours (N decided in the plan),
52: the chance of rain and the temperature, from recent observed hours plus time
53: features. Compare it against a simple baseline on a held-out period. Recommend
54: local TabPFN or the TabPFN API client, with the trade-off; I choose.
55:
56: Acceptance criteria:
57: - One command prints the held-out metrics for TabPFN AND the baseline, on the
58: same split, with the split dates.
59: - The forecast function returns per-hour rain probability and temperature
60: for the next N hours.
61: - The metrics, the command and the split are written to the journal.
62: - No claim of accuracy anywhere that the printed metrics do not support.
63:
64: ## M4 - Local plan text with Ollama (Wed 2026-10-07)
65:
66: Turn the forecast plus an activity (run, walk, garden, errands) into a 2-3
67: sentence plan in plain language, using the M1 model through Ollama.
68:
69: Acceptance criteria:
70: - One command prints a plan for a given activity from a real forecast.
71: - The prompt lives in one file in the repo, not scattered through code.
72: - The plan names a specific time window and the reason (rain or heat).
73: - If Ollama is not running or the model is not pulled, the command exits with
74: a one-line message saying which, not a stack trace.
75:
76: ## M5 - Voice with ElevenLabs (Thu 2026-10-08)
77:
78: Read the plan aloud with ElevenLabs text-to-speech.
79:
80: Acceptance criteria:
81: - One command produces an audio file of today's plan and plays it or prints
82: its path.
83: - The key comes from .env only.
84: - If the key is missing or the call fails, the plan text is still printed and
85: the command says audio was skipped.
86: - No audio files or keys are committed.
87:
88: ## M6 - One-tap morning brief + real use (Thu-Fri 2026-10-08 to 2026-10-09)
89:
90: Give me the simplest way to get the brief without looking at a screen for
91: long. Recommend CLI-only or a tiny local page with one "Play today's brief"
92: button; I choose. Then I use it outside on at least two mornings.
93:
94: Acceptance criteria:
95: - From a cold start, one action gives me the spoken brief.
96: - The README quick start works on a clean clone (you verify by following it).
97: - My notes from each morning are in the journal, in my words.
98:
99: ## M7 - Submission pack (Sat-Sun 2026-10-10 to 2026-10-11)
100:
101: Get everything ready for me to publish. You do not publish anything.
102:
103: Acceptance criteria:
104: - skills/secrets-check.md passes on the repo and on every session I will
105: embed.
106: - README is final: what it does, quick start, how it is built, credits,
107: "Changes after submission".
108: - A post draft in journal/w1-post-draft.md that follows the submission
109: template sections, built only from journal facts.
110: - A list of the agent session slices to embed, with their links.
111: - Prize Categories section lists only categories genuinely used.
112:
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Created At: 2026-10-06T16:20:16+01:00
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48: Source：https://dev.to/g00ds0ul/my-weather-app-says-60-rain-all-day-so-i-made-tabpfn-tell-me-which-hours-to-go-touch-grass-27l5