W1 M4 - Local plan text with Ollama

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1: # Memory index
2:
3: <!-- generated by tools/build-memory-index.py - do not edit by hand -->
4: <!-- fingerprint: d0dd490e159bb5fa -->
5: <!-- generated: 2026-10-07 -->
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: 6 entries, D-1 to D-7. Next free id: D-8. Shards: decisions.md
22:
23: Not present: D-6. Never reuse them.
24:
25: - 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]
26: - 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]
27: - 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]
28: - D-4 Week 1 specific model is Qwen 2.5 7B [decisions.md]
29: - 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]
30: - D-7 Use n_estimators=2 for TabPFN - Use n_estimators=2 everywhere for TabPFN predictions [decisions.md]
31:
32: ## Patterns - conventions and non-obvious discoveries.
33:
34: 3 entries, P-1 to P-3. Next free id: P-4. Shards: patterns.md
35:
36: - 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]
37: - 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]
38: - 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]
39:
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1: # Skills
2:
3: A skill is a checklist for one recurring job. It is opened when a task
4: matches it and stops being relevant when that task ends.
5:
6: - AGENTS.md holds rules and routing. Short.
7: - memory/index.md holds one line per memory entry. Always read.
8: - memory/*.md hold the full entries. Opened when the index says to.
9: - skills/*.md hold procedure. Opened when the task matches.
10:
11: Do not move rules into skills. Do not move procedure into AGENTS.md.
12:
13: ## How a skill gets selected
14:
15: Antigravity: there is no automatic skill selection. This dispatch table
16: is the whole mechanism. You are required to consult it.
17:
18: Claude Code: skills are registered at .claude/skills/<name>/SKILL.md.
19: Each is a stub that points here, so both tools follow the same text.
20:
21: ## Dispatch table
22:
23: Check this table before you write a plan file. Match on what the task is,
24: not on what it is called.
25:
26: | If the task is | Open |
27: | --- | --- |
28: | Planning any change to a week repo | skills/adversarial-review.md |
29: | Fixing a reported bug or regression | skills/regression-first.md |
30: | Deciding whether work is finished | skills/verify-before-done.md |
31: | About to commit, push, upload a session, or hand me post text | skills/secrets-check.md |
32: | I confirmed a milestone, or I asked you to save the session | skills/milestone-close.md |
33: | Writing to memory/ for any reason | skills/memory-write.md |
34: | The same instruction has been given three or more times | skills/extract-skill.md |
35:
36: If two rows match, follow both, in the order listed.
37:
38: If no row matches, say no skill matches in one line and proceed under
39: AGENTS.md. Do not invent a skill.
40:
41: ## Rules of use
42:
43: 1. Naming a skill is not using it. Open the file and follow its steps in
44: order.
45: 2. One skill drives at a time. If a skill sends you to another, finish
46: the step you are on, switch, and say that you switched.
47: 3. A skill never overrides AGENTS.md or memory/.
48: 4. Report which step you are on, once per skill:
49: skill: <name>, step <n> of <m>.
50: 5. A skill is not approval. It never satisfies the APPROVE gate.
51:
52: ## Adding a skill
53:
54: Use skills/extract-skill.md. Add one only when the same correction has been
55: issued three or more times.
56:
57: Every skill file uses this shape, in this order:
58:
59:
60: # <name>
61: ## When to use
62: ## What you must have first
63: ## Steps
64: ## Output
65: ## Stop conditions
66: ## Do not
67:
68:
69: If a skill grows past roughly 150 lines, it is doing two jobs. Split it.
70:
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1: # Decisions
2:
3: Choices made between alternatives, including what was rejected and why.
4:
5: ### D-1 - Python is the stack for every week
6: - Decision: Python 3.12 with uv, pytest, ruff and python-dotenv for all four challenge repos unless a week's build plan says otherwise.
7: - Rejected: Node.js (my default before this month) - TabPFN only ships a Python package, pandas is the natural fit for weather CSVs, and Node would have meant running a Python sidecar anyway. pip/Poetry/conda - uv is faster and one tool.
8: - Applies to: all repos
9: - Status: active
10: - Date: 2026-10-05
11:
12: ### D-2 - One new repo per challenge, in the G00dS0ul-Hackerton org
13: - Decision: Each challenge gets its own new repo (w1-..., w2-..., w3-..., w4-...) under the G00dS0ul-Hackerton GitHub org, created inside that challenge's window. This workspace root holds the agent kit and is not part of any repo.
14: - Rejected: One "hakerton" monorepo for the month - the rules require each submission's repository to be started and completed inside its challenge window, and a shared repo would carry commits from before each window opened.
15: - Applies to: all repos
16: - Status: active
17: - Date: 2026-10-05
18:
19: ### D-3 - Week 1 model is open-weight and not Gemma
20: - Decision: Week 1 uses an open-weight model through Ollama that is not Gemma (Qwen or Llama family; the exact model is chosen in M1). Target prize categories are TabPFN and ElevenLabs.
21: - Rejected: Gemma - my Weekend Challenge entry used it and the Gemma category looked crowded in the submissions. Closed APIs for the core - the challenge requires open-source AI at the core.
22: - Applies to: w1-go-out-today
23: - Status: active
24: - Date: 2026-10-05
25:
26: ### D-4 - Week 1 specific model is Qwen 2.5 7B
27: - Status: SUPERSEDED by D-5
28:
29: ### D-5 - Week 1 specific model deferred to M4
30: - Decision: The final choice between qwen2.5:7b and qwen2.5-coder:7b will be made during Milestone 4 (M4) after testing with the real Python prompt pipeline. Both had ~10s medians in early tests.
31: - Rejected: qwen2.5-coder:3b - dropped due to poor answer logic (advising runs during rain).
32: - Applies to: w1-go-out-today
33: - Status: active
34: - Date: 2026-10-06
35:
36: - D-6 Local TabPFN on CPU chosen over TabPFN API client - Ise explicitly requested using the local CPU-only PyTorch build for TabPFN, reducing the training set to at most 5k rows to fit the CPU limit, instead of using the TabPFN API client.
37: - Trigger: TabPFN forecast M3 execution
38: - Repo: w1-go-out-today
39: ### D-7 - Use n_estimators=2 for TabPFN
40: - Decision: Use n_estimators=2 everywhere for TabPFN predictions.
41: - Rejected: n_estimators=10 and tabpfn-client. n=10 took ~8 min to run compared to ~3.6 min for n=2, yet yielded the exact same accuracy metrics (MAE 0.712 vs 0.719, Brier 0.205 vs 0.203, AUC 0.733 both).
42: - Applies to: w1-go-out-today
43: - Status: active
44: - Date: 2026-10-06
45:
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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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1: # M3 TabPFN forecast
2:
3: - Task: w1-M3
4: - Status: IMPLEMENTED - AWAITING ISE CONFIRMATION
5: - Created: 2026-10-06
6: - Updated: 2026-10-06
7: - Repo: w1-go-out-today
8:
9: ## 1. Task
10:
11: Train ONE local TabPFN classifier and ONE regressor on CPU to predict per-hour rain probability (ROC AUC, Brier) and temperature (MAE) for hours ahead N=1..12. Training data is up to 3,000 (issue time, hours ahead) pairs. Test on the last 30 days at 06:00 issue times. Evaluate at default and n_estimators=2.
12:
13: ## 2. Memory that applies
14:
15: - D-1 - Use uv for python dependencies. We will configure pyproject.toml to use uv's custom index feature to pull PyTorch CPU wheels.
16: - D-6 - Chose local TabPFN running on CPU instead of the TabPFN API client, per Ise's choice.
17: - P-1 - I will not mark the milestone or plan as Done, only stop at AWAITING ISE CONFIRMATION.
18:
19: ## 3. Files affected
20:
21: | Repo | Path | Action | Notes |
22: | --- | --- | --- | --- |
23: | w1-go-out-today | pyproject.toml | modify | Add tabpfn, scikit-learn and configure tool.uv.index for pytorch-cpu |
24: | w1-go-out-today | .env.example | modify | Add TABPFN_TOKEN placeholder |
25: | w1-go-out-today | src/w1_go_out_today/forecast.py | create | TabPFN setup, split logic, baselines, metrics, and multi-estimator loops |
26: | w1-go-out-today | tests/test_forecast.py | create | Smoke test for forecast logic |
27: | w1-go-out-today | README.md | modify | Add instructions, TabPFN version, and license details |
28:
29: ## 4. Interfaces
30:
31: | Interface | Status | Evidence |
32: | --- | --- | --- |
33: | tabpfn.TabPFNClassifier.fit/predict_proba | confirmed in installed package | .venv/Lib/site-packages/tabpfn/classifier.py:949 and :1692 |
34: | tabpfn.TabPFNRegressor.fit/predict | confirmed in installed package | .venv/Lib/site-packages/tabpfn/regressor.py:1280 and :1370 |
35: | uv custom package indexes | confirmed in official docs | Verified via research subagent for pytorch-cpu config |
36:
37: ## 5. Scope check
38:
39: Milestone M3 from docs/challenge/w1/build-plan.md. Acceptance criteria, quoted verbatim:
40:
41: > - One command prints the held-out metrics for TabPFN AND the baseline, on the
42: > same split, with the split dates.
43: > - The forecast function returns per-hour rain probability and temperature
44: > for the next N hours.
45: > - The metrics, the command and the split are written to the journal.
46: > - No claim of accuracy anywhere that the printed metrics do not support.
47:
48: ## 6. Steps
49:
50: 1. Modify .env.example to include a TABPFN_TOKEN= placeholder.
51: 2. Modify pyproject.toml to add scikit-learn, tabpfn, and configure torch to use the CPU index. Verify with uv sync.
52: 2.5. Re-verify the TabPFN installed interfaces (path:line) and update Section 4.
53: 3. Write src/w1_go_out_today/forecast.py implementing:
54: - forecast(df, issue_time, n=12) function returning per-hour rain prob and temp.
55: - Features at issue time: all M2 variables + 3h change in pressure/humidity + rain in last 3h + target hour + target month + hours ahead (1-12).
56: - Test set: issue_time at 06:00 every day in the last 30 days. ~360 rows.
57: - Training: up to 3000 pairs from the 12 months before the test period.
58: - Baselines: Climatology (all data before test period) and Persistence.
59: - Metric calculation per hours-ahead value and overall average, timing at default and n_estimators=2.
60: 4. Write tests/test_forecast.py to test features, targets, splits, and baselines on a fixture. TabPFN will be stubbed.
61: 5. Add a CLI entry point to execute the evaluation from the latest row in the CSV.
62: 6. Run the script. Stop if full run > 5 mins.
63: 7. Update README.md (TabPFN version, license URL, CLI command). Add journal notes including "Later" note about real data.
64:
65: ## 7. Time cost
66:
67: ~3 h. Fits M3.
68:
69: ## 8. Objections
70:
71: ### O-1 Data Size and Context Limit - minor
72: Objection: TabPFN context limits may struggle.
73: Resolution: Sampling to 3000 rows.
74:
75: ### O-2 Target leakage - blocker
76: Objection: Predicting future states risks leakage.
77: Resolution: Strict test sets at 06:00 with shifting. Tests will verify no future leakage.
78:
79: ### O-3 Climatology Baseline Logic - minor
80: Objection: Climatology needs to use all past data.
81: Resolution: Explicitly grouped historical means on all pre-test data.
82:
83: ## 9. Verdict
84:
85: READY FOR APPROVAL - Awaiting approval. I will not write code until you reply APPROVE.
86:
87: ## 10. Approval
88:
89: - Approved: 2026-10-06. "APPROVE WITH CHANGES Forecast horizon N=12 (06:00–18:00 brief)..."
90: - Changes requested: See exact words integrated into Steps.
91: - Confirmed: 2026-10-06. "Sample‑size check done. Keep 3,000 as the default. 5,000 was no better and took 2× longer. Note in the journal... mark M3 done."
92:
93: ## 11. Implementation log
94:
95: - [ ] Step 1
96: - [ ] Step 2
97: - [ ] Step 2.5
98: - [ ] Step 3
99: - [ ] Step 4
100: - [ ] Step 5
101: - [ ] Step 6
102: - [ ] Step 7
103:
104: ## 12. Deviations
105:
106: | Expected | Found | Did | Why the plan missed it |
107: | --- | --- | --- | --- |
108: | TabPFN interactive login prompts browser on missing token | TabPFN crashed with WinError 10038 because select.select([sys.stdin]) fails on Windows | Added load_dotenv() so token from .env is loaded automatically, bypassing prompt. Added warning to README | Did not foresee that a library's auth logic uses Linux-only select patterns on standard input |
109: | prepare_data creates matched targets correctly | Forecasting from the latest row crashed because prepare_data blindly dropped rows without future targets | Added is_training=True flag to optionally skip dropping NaN targets during live inference, plus a regression test | Assumed the same function could process history and live inference without accounting for unobserved future targets |
110:
111: ## 13. Memory written
112:
113: - D-6 - Chose local TabPFN running on CPU instead of the TabPFN API client.
114:
115: ## 14. For Ise to confirm
116:
117: - Command prints metrics (ROC AUC, Brier, MAE) and split dates.
118: - Output times training and prediction for the two n_estimators settings.
119: - README mentions TabPFN version and license.
120: - The metrics, command, and split are written to the journal.
121:
122: ## 15. Journal + session
123:
124: - Journal entry: journal/w1-go-out-today.md - not yet
125: - Agent session: https://dev.to/agent_sessions/w1-m3-tabpfn-rain-forecast-for-lagos-rt4xnt (private)
126:
127: ---
128:
129: ## Revision 2 - Actual vs Forecast view
130:
131: - Task: Add an "actual vs forecast" view to the forecast CLI for past issue times.
132: - Status: IMPLEMENTED - AWAITING ISE CONFIRMATION
133:
134: ### R2.1 Scope
135: When the issue time is in the past, fetch real targets (temp, rain > 0 mm) and display them alongside the forecast. Includes a one-line summary of average temp error and rainy hours with >50% chance. Must never leak actuals into model inputs.
136:
137: ### R2.2 Files affected
138: | Repo | Path | Action | Notes |
139: | --- | --- | --- | --- |
140: | w1-go-out-today | src/w1_go_out_today/forecast.py | modify | Add actuals lookup and summary line logic |
141: | w1-go-out-today | tests/test_forecast.py | modify | Test proving actuals are not passed to model |
142:
143: ### R2.3 Steps
144: 1. Write a test in test_forecast.py (test_forecast_no_actuals_leakage) proving the dataframe passed into forecast() does not use future targets to generate predictions.
145: 2. In forecast.py (run_forecast_evaluation or via a new display wrapper), after forecast() returns fcst, look up target_time = issue_time + hours_ahead in df.
146: 3. If actuals exist, map actual_temp and actually_rained (precipitation > 0).
147: 4. Update CLI output formatting (columns: time | forecast temp | actual temp | rain chance | actually rained?). Keep 07:00 -> 18:00 sorting.
148: 5. Compute and print the summary line: average temp error and count of rainy hours with >50% predicted chance.
149: 6. If no actuals exist, print just the forecast.
150:
151: ### R2.4 Objections
152: - O-1 Data Leakage - blocker: Fetching actuals could accidentally leak future targets into the model.
153: Resolution: The forecast() function signature and internal prepare_data call do not receive or bind to the actuals. The actuals lookup happens entirely after the forecast() returns the dataframe.
154:
155: ### R2.5 Verdict
156: READY FOR APPROVAL - Awaiting approval. I will not write code until you reply APPROVE.
157:
158: ---
159:
160: ## Revision 3 - Sample size check
161:
162: - Task: Run a quick sample-size check (1,000, 3,000, 5,000 rows) for TabPFN.
163: - Status: IMPLEMENTED - AWAITING ISE CONFIRMATION
164:
165: ### R3.1 Scope
166: Add a --sample-size-check flag to the forecast CLI that tests 1,000, 3,000, and 5,000 training rows sequentially using the same seed, n_estimators=2, and test set. Outputs a summary table: rows | Temp MAE | Rain AUC | Rain Brier | total time.
167:
168: ### R3.2 Files affected
169: | Repo | Path | Action | Notes |
170: | --- | --- | --- | --- |
171: | w1-go-out-today | src/w1_go_out_today/forecast.py | modify | Add the sample size check loop triggered by --sample-size-check |
172:
173: ### R3.3 Steps
174: 1. Modify forecast.py to add a new function run_sample_size_check() that calls get_splits but modifies the n argument for .sample(n=size) (I'll need to update get_splits to accept a train_size parameter, defaulting to 3000).
175: 2. For size in [1000, 3000, 5000], run train_and_evaluate (which I'll modify slightly to return the overall MAE, ROC, and Brier, or just extract them).
176: 3. Print the formatted table.
177: 4. If the user passes --sample-size-check, run this and exit.
178: 5. Record the results into journal/w1-go-out-today.md under the M3 section.
179: 6. Keep 3000 as the default in get_splits unless the result indicates 5000 is significantly better and completes in under 5 minutes.
180:
181: ### R3.4 Objections
182: - O-1 Redundant Evaluation output - minor: train_and_evaluate prints a lot of verbose hourly breakdown lines.
183: Resolution: I will add a quiet=True flag to train_and_evaluate so the sample size check table isn't lost in terminal spam.
184:
185: ### R3.5 Verdict
186: READY FOR APPROVAL - Awaiting approval. I will not write code until you reply APPROVE.
187:
The above content shows the entire, complete file contents of the requested file.

Created At: 2026-10-07T13:50:15+01:00
Completed At: 2026-10-07T13:50:15+01:00
File Path: file:///[REDACTED_PATH]/G00dS0ul-Hackerton/plans/README.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: # Plans
2:
3: One file per planned task, produced by .agents/rules/plan-before-build.md.
4: They record what was planned, what was objected to, what I approved, and where
5: reality diverged. A later session must be able to pick up an approved plan and
6: continue without re-deriving it.
7:
8: ## Naming
9:
10: - w1-M3-tabpfn-forecast.md - milestone work (week + milestone id)
11: - w1-fix-empty-csv-crash.md - a fix with no milestone
12:
13: One task per file. Never overwrite an existing plan file.
14:
15: ## Status
16:
17: DRAFT -> AWAITING APPROVAL -> APPROVED -> IN PROGRESS ->
18: IMPLEMENTED - AWAITING ISE CONFIRMATION
19:
20: Also valid: BLOCKED, ABANDONED, SUPERSEDED BY <path>
21:
22: There is no DONE status. Only I confirm completion.
23:
24: ## Template
25:
26: markdown
27: # <Title>
28:
29: - **Task:** w1-M<n> or Fix: <one line>
30: - **Status:** DRAFT
31: - **Created:** YYYY-MM-DD
32: - **Updated:** YYYY-MM-DD
33: - **Repo:** w1-go-out-today
34:
35: ## 1. Task
36:
37: One sentence, as understood.
38:
39: ## 2. Memory that applies
40:
41: - C-n / D-n / P-n - what it requires here
42:
43: ## 3. Files affected
44:
45: | Repo | Path | Action | Notes |
46: | --- | --- | --- | --- |
47: | w1-go-out-today | `src/forecast.py` | create | |
48: | w1-go-out-today | `src/brief.py:41` | modify | |
49:
50: ## 4. Interfaces
51:
52: | Interface | Status | Evidence |
53: | --- | --- | --- |
54: | `tabpfn.TabPFNClassifier.fit/predict_proba` | confirmed in installed package | `.venv/.../tabpfn/classifier.py:<line>` |
55: | ElevenLabs text-to-speech | confirmed in official docs | <url> |
56: | Ollama chat | NOT CONFIRMED | stop |
57:
58: ## 5. Scope check
59:
60: Milestone M<n> from `docs/challenge/w1/build-plan.md`. Acceptance criteria,
61: quoted verbatim:
62:
63: > ...
64:
65: ## 6. Steps
66:
67: 1. What changes, which files, how verified (command).
68:
69: ## 7. Time cost
70:
71: ~<n> h. Fits / pushes M<n>.
72:
73: ## 8. Objections
74:
75: ### O-1 <title> - blocker | serious | minor
76:
77: **Objection:** ...
78: **Resolution:** amended the plan / accepted because ...
79:
80: (minimum three, or two for a short-form plan)
81:
82: ## 9. Verdict
83:
84: BLOCKED - numbered questions
85: or
86: READY FOR APPROVAL - Awaiting approval. I will not write code until you reply APPROVE.
87:
88: ## 10. Approval
89:
90: - **Approved:** YYYY-MM-DD, Ise's exact words
91: - **Changes requested:** ...
92:
93: ## 11. Implementation log
94:
95: - [x] Step 1 - `w1-go-out-today/src/forecast.py:12`
96: - [ ] Step 2
97:
98: ## 12. Deviations
99:
100: | Expected | Found | Did | Why the plan missed it |
101: | --- | --- | --- | --- |
102:
103: ## 13. Memory written
104:
105: - C-n / D-n / P-n - one line each, or "none"
106:
107: ## 14. For Ise to confirm
108:
109: - What to check, against the criteria in section 5.
110:
111: ## 15. Journal + session
112:
113: - Journal entry: `journal/w1-go-out-today.md` - <checkpoint title>, or "not yet"
114: - Agent session: <dev.to link> (private), or "not saved - <reason>"
115:
116:
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Created At: 2026-10-07T13:50:17+01:00
Completed At: 2026-10-07T13:50:17+01:00
File Path: file:///[REDACTED_PATH]/G00dS0ul-Hackerton/skills/adversarial-review.md
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Total Bytes: 2895
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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: # adversarial-review
2:
3: How the plan gets written. .agents/rules/plan-before-build.md says a plan
4: is required and holds the approval gate. This file is the procedure.
5:
6: ## When to use
7:
8: Any task that would create, modify or delete files in a week repo.
9:
10: Not for: answering questions, reading code, writing to memory/, plans/
11: or journal/.
12:
13: ## What you must have first
14:
15: 1. memory/index.md read, and every entry whose line touches this task
16: opened.
17: 2. The milestone in docs/challenge/<week>/build-plan.md this task belongs
18: to, with its acceptance criteria.
19: 3. The files you intend to change, actually read.
20: 4. For every library or service the plan calls: the installed package source
21: or the official docs page, actually opened.
22:
23: ## Steps
24:
25: 1. Gather in parallel. Issue reads at the same time. Reading is
26: parallel. Writing is serial.
27: 2. Write the plan file from plans/README.md, before saying anything
28: substantive in chat.
29: 3. Raise three objections. Use at least two different kinds:
30: - Scope: adds something the milestone did not ask for.
31: - Evidence: rests on an assumption I have not verified.
32: - Interface: calls a library or API shape I have not confirmed.
33: - Rules: risks the challenge rules or a secret.
34: - Time: does not fit the day it is scheduled for.
35: - Simpler path: the same outcome with less.
36: 4. Answer each objection in writing. Fix the plan or say why it does not
37: hold. "Noted" is not an answer.
38: 5. Leanness test. Delete anything that is an abstraction with one caller,
39: a config value read once, a class where a function will do, a new file
40: where an existing one will do, a helper nobody asked for, or error
41: handling for a condition you have not shown can occur. The exceptions are
42: the failure messages the acceptance criteria require.
43: 6. Verdict: PROCEED, PROCEED WITH CHANGES, or DO NOT PROCEED (say
44: what would change your mind). Map it to the file's BLOCKED or
45: READY FOR APPROVAL.
46: 7. Stop at the gate. Set AWAITING APPROVAL and stop.
47: 8. After approval, do not stop again until every step is done.
48:
49: ## Output
50:
51: In chat, three things only: the plan file path, the verdict, and the
52: objections with their answers, one line each.
53:
54: ## Stop conditions
55:
56: 1. The approval gate.
57: 2. A step cannot be done as planned. Say which, what blocks it, and the
58: amended step.
59: 3. A genuine blocker: missing key, model not pulled, failing build you did
60: not cause.
61: 4. A decision that is mine.
62:
63: The task being long is not a stop condition. If you are about to write
64: "would you like me to continue", the answer is yes.
65:
66: ## Do not
67:
68: - Do not claim anything about the code without path:line.
69: - Do not claim anything about a library without the file or docs URL.
70: - Do not claim something is absent without pasting the search.
71: - Do not soften an objection because you want the plan approved.
72:
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Created At: 2026-10-07T13:50:20+01:00
Completed At: 2026-10-07T13:50:20+01:00
File Path: file:///[REDACTED_PATH]/G00dS0ul-Hackerton/journal/w1-go-out-today.md
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1: # Week 1 - Go Out Today? - build journal
2:
3: Challenge: Hacktoberfest Open-Source AI Challenge Week 1, "Touch Grass"
4: Window: 2026-10-05 to 2026-10-11
5:
6: ### Checkpoint 0 - Idea, stack and workspace - 2026-10-05
7: - What happened: Picked the idea: a voice-first morning brief that tells
8: people in Lagos the best time to go outside. Chose Python, TabPFN, Ollama
9: with a non-Gemma open model, and ElevenLabs. Set up DevRelay in
10: Antigravity and tested a private agent-session upload. Set up this agent
11: workspace.
12: - Numbers: none
13: - Broke / fixed: DevRelay's Unix installer does not run in Git Bash on
14: Windows (used the PowerShell installer). devrelay login auto-open launched
15: File Explorer instead of the browser (opened the URL by hand). The installer
16: did not register with Antigravity (added devrelay-gateway to
17: .gemini/config/mcp_config.json by hand).
18: - Screenshot to take: none
19: - Plan: none
20: - Session: test session only, private, not for the post
21:
22: ### Checkpoint 1 - M1 Set up the repo - 2026-10-06
23: - What happened: Set up the w1-go-out-today project using uv for Python 3.12, including pytest, ruff, and python-dotenv. Ran a simple prompt test to pick an Ollama model for the plain-language forecast. Dropped the 3B coder model due to poor logic constraints (advising runs during rain). The 7B coder model had okay answers, and both qwen2.5:7b and qwen2.5-coder:7b clocked ~10s medians, so we are deferring the final decision between those two until we reach Milestone 4.
24: - Numbers: 4 models available locally; 3B model dropped; 7B models clocking ~10s medians.
25: - Broke / fixed: Global uv was missing; installed uv via the official PowerShell script to ~/.local/bin instead of assuming PATH.
26: - Screenshot to take: The 5-iteration prompt test comparing Qwen models.
27: - Plan: plans/w1-M1-set-up-repo.md
28: - Session: https://dev.to/agent_sessions/w1-m1-set-up-the-repo-updated-plltsz
29:
30: ### Checkpoint 2 - M2 Lagos weather data - 2026-10-06
31: - What happened: Implemented a script (fetch_weather.py) to download 3 years of hourly historical weather data for Lagos from the Open-Meteo Archive API. The fetch pulls temperature, humidity, dew point, precipitation, cloud cover, pressure, and wind speed. Data is saved as data/lagos_weather.csv. Added testing via a local JSON fixture to ensure missing values are properly dropped (without touching the network).
32: - Numbers: Downloaded 3 years (2023-10-01 to 2026-09-30) of hourly data, yielding 26,304 rows. Dropped 0 rows in the real run.
33: - Broke / fixed: First plan draft assumed "past 12 months" and only two variables. Corrected to specific variables, a fixed 3-year date range, and timezone=Africa/Lagos.
34: - Screenshot to take: The test suite running cleanly on the local fixture data.
35: - Plan: plans/w1-M2-lagos-weather.md
36: - Session: https://dev.to/agent_sessions/w1-m2-lagos-weather-data-jhbfln
37:
38: ### Checkpoint 3 - M3 TabPFN forecast - 2026-10-06
39: - What happened: Set up local TabPFN (v9.1.0/v3.5) with uv pytorch-cpu index to predict rain and temperature for 1-12 hours ahead. Implemented a complex test split (last 30 days, 06:00 issue times only) against a 12-month trailing train set (capped at 3000 rows), ensuring no future data leakage. Evaluated against climatology (computed from all data before the test split) and persistence baselines. Added tests with a TabPFN stub.
40: - Numbers: Train rows: 3000, Test rows: 360 (from 2026-09-01 06:00:00 to 2026-09-30 06:00:00).
41: - Climatology baseline: Temp MAE=0.750, Rain ROC=0.606, Rain Brier=0.231
42: - Persistence baseline: Temp MAE=1.608, Rain ROC=0.663, Rain Brier=0.322
43: - Evaluated TabPFN locally comparing n_estimators=10 vs 2. Decided to use n_estimators=2 everywhere as it gave the same accuracy (MAE 0.712 vs 0.719, Brier 0.205 vs 0.203, AUC 0.733 both) but took ~3.6 min compared to ~8 min.
44: - Ran a sample size check (n=2):
45: text
46: === Sample Size Check (n_estimators=2) ===
47: Rows | Temp MAE | Rain AUC | Rain Brier | Total Time
48: -------------------------------------------------------
49: 1000 | 0.627 | 0.685 | 0.222 | 27.90s
50: 3000 | 0.712 | 0.733 | 0.205 | 96.79s
51: 5000 | 0.691 | 0.728 | 0.207 | 189.12s
52:
53: Note: 1,000 rows gave better temp MAE (0.627) but worse rain, from one seed, and we didn't tune on it to avoid overfitting the test set. More data didn't mean better forecasts: 5,000 rows took twice as long as 3,000 and scored the same. Temperature was even slightly better with 1,000. TabPFN gets most of its value from a small sample. Decided to stick with 3000 rows as the default.
54: - Insight for DEV post: On Sep 30 it called a wet midday that never came. The rain showed up at 5pm. Over 30 days, though, it beat both baselines. That's why the brief says "80% chance", not "it will rain".
55: - Broke / fixed: threadpoolctl crashed loading OpenBLAS DLL due to the 🤡 emoji in the Windows path (FileNotFoundError); patched _find_libraries_on_windows to bypass. TabPFN interactive browser login crashed on Windows (WinError 10038 from select.select on sys.stdin); fixed by ensuring load_dotenv() runs before TabPFN imports. The live forecasting CLI crashed because prepare_data was dropping rows with missing future targets; fixed by adding an is_training=True/False flag and adding a regression test.
56: - Screenshot to take: The CLI command outputting the baselines.
57: - Plan: plans/w1-M3-tabpfn-forecast.md
58: - Session: https://dev.to/agent_sessions/w1-m3-tabpfn-rain-forecast-for-lagos-rt4xnt (private)
59:
60: ## Later
61: - The Open-Meteo historical CSV ends 2026-09-30, so the real app needs recent observations (for M6).
62:
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Created At: 2026-10-07T13:50:22+01:00
Tool is running as a background task with task id: e9cc7112-54e0-4d02-b5fd-28eee49d8c50/task-15
Task Description: Get-ChildItem -Recurse -File | Select-Object -ExpandProperty FullName
Task logs are available at: file:///[REDACTED_PATH]/.gemini/antigravity/brain/e9cc7112-54e0-4d02-b5fd-28eee49d8c50/.system_generated/tasks/task-15.log
YOU MUST TAKE ONE OF THE FOLLOWING TWO ACTIONS: A) either proceed to other relevant work (if any) or, B) simply update the user with a short message (that you have launched the command and will wait for it to finish) and end the turn.
DO NOTHING ELSE.

Created At: 2026-10-07T13:51:38+01:00
Completed At: 2026-10-07T13:51:38+01:00
File Path: file:///[REDACTED_PATH]/G00dS0ul-Hackerton/w1-go-out-today/pyproject.toml
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Total Bytes: 779
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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: [project]
2: name = "w1-go-out-today"
3: version = "0.1.0"
4: description = "Add your description here"
5: readme = "README.md"
6: authors = [
7: { name = "G00dS0ul", email = "[REDACTED]" }
8: ]
9: requires-python = ">=3.12"
10: dependencies = [
11: "pandas>=3.0.6",
12: "python-dotenv>=1.2.4",
13: "scikit-learn>=1.5.0",
14: "tabpfn>=2.0.0",
15: "torch>=2.0.0"
16: ]
17:
18: [project.scripts]
19: w1-go-out-today = "w1_go_out_today:main"
20:
21: [build-system]
22: requires = ["uv_build>=0.12.23,<0.13.0"]
23: build-backend = "uv_build"
24:
25: [dependency-groups]
26: dev = [
27: "pytest>=9.1.1",
28: "ruff>=0.16.10",
29: ]
30:
31: [[tool.uv.index]]
32: name = "pytorch-cpu"
33: url = "https://download.pytorch.org/whl/cpu"
34: explicit = true
35:
36: [tool.uv.sources]
37: torch = [{ index = "pytorch-cpu" }]
38:
The above content shows the entire, complete file contents of the requested file.

Created At: 2026-10-07T13:51:38+01:00
Completed At: 2026-10-07T13:51:39+01:00
File Path: file:///[REDACTED_PATH]/G00dS0ul-Hackerton/w1-go-out-today/src/w1_go_out_today/forecast.py
Total Lines: 276
Total Bytes: 12066
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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: import time
2: import os
3: import pandas as pd
4: import numpy as np
5: from sklearn.metrics import mean_absolute_error, roc_auc_score, brier_score_loss
6: from datetime import timedelta
7:
8: from dotenv import load_dotenv
9: load_dotenv()
10:
11: # Monkeypatch threadpoolctl to fix Windows paths with emojis breaking ctypes
12: import threadpoolctl
13: threadpoolctl.ThreadpoolController._find_libraries_on_windows = lambda self: None
14:
15: from tabpfn import TabPFNClassifier, TabPFNRegressor
16:
17: def prepare_data(df, n=12, is_training=True):
18: df = df.copy()
19: if 'time' in df.columns:
20: df['time'] = pd.to_datetime(df['time'])
21: df = df.sort_values('time').reset_index(drop=True)
22: df.set_index('time', inplace=True)
23:

24: df['press_diff_3h'] = df['pressure_msl'] - df['pressure_msl'].shift(3)
25: df['hum_diff_3h'] = df['relative_humidity_2m'] - df['relative_humidity_2m'].shift(3)
26: df['rain_last_3h'] = df['precipitation'].rolling(3).sum()
27:

28: base_cols = [
29: 'temperature_2m', 'relative_humidity_2m', 'dew_point_2m',
30: 'precipitation', 'cloud_cover', 'pressure_msl', 'wind_speed_10m',
31: 'press_diff_3h', 'hum_diff_3h', 'rain_last_3h'
32: ]
33:

34: df_features = df[base_cols].dropna()
35:

36: dfs = []
37: for h in range(1, n + 1):
38: df_h = df_features.copy()
39: df_h['hours_ahead'] = h
40:

41: target_time = df_h.index + timedelta(hours=h)
42: df_h['target_hour'] = target_time.hour
43: df_h['target_month'] = target_time.month
44:

45: if is_training:
46: targets = df[['temperature_2m', 'precipitation']].reindex(target_time)
47: targets.index = df_h.index
48: df_h['temp_target'] = targets['temperature_2m']
49: df_h['rain_target'] = (targets['precipitation'] >= 0.1).astype(int)
50: df_h = df_h.dropna(subset=['temp_target', 'rain_target'])
51:

52: dfs.append(df_h)
53:

54: final_df = pd.concat(dfs).sort_index()
55: return final_df
56:
57: def get_splits(df_pairs, train_size=3000):
58: max_time = df_pairs.index.max()
59: test_start = max_time - timedelta(days=30)
60:

61: df_pre_test = df_pairs[df_pairs.index < test_start].copy()
62: df_test = df_pairs[(df_pairs.index >= test_start) & (df_pairs.index.hour == 6)].copy()
63:

64: train_start = test_start - timedelta(days=365)
65: df_train_pool = df_pre_test[df_pre_test.index >= train_start].copy()
66:

67: if len(df_train_pool) > train_size:
68: df_train = df_train_pool.sample(n=train_size, random_state=42)
69: else:
70: df_train = df_train_pool
71:

72: return df_train, df_test, df_pre_test
73:
74: def build_climatology(df_pre_test):
75: clim_temp = df_pre_test.groupby(['target_month', 'target_hour'])['temp_target'].mean().reset_index()
76: clim_rain = df_pre_test.groupby(['target_month', 'target_hour'])['rain_target'].mean().reset_index()
77:

78: clim = pd.merge(clim_temp, clim_rain, on=['target_month', 'target_hour'], suffixes=('', '_rain'))
79: clim.rename(columns={'temp_target': 'clim_temp', 'rain_target': 'clim_rain_prob'}, inplace=True)
80: return clim
81:
82: def train_and_evaluate(df_train, df_test, df_pre_test, n_estimators=2, quiet=False):
83: features = [
84: 'temperature_2m', 'relative_humidity_2m', 'dew_point_2m',
85: 'precipitation', 'cloud_cover', 'pressure_msl', 'wind_speed_10m',
86: 'press_diff_3h', 'hum_diff_3h', 'rain_last_3h',
87: 'hours_ahead', 'target_hour', 'target_month'
88: ]
89:

90: X_train = df_train[features]
91: y_train_temp = df_train['temp_target']
92: y_train_rain = df_train['rain_target']
93:

94: X_test = df_test[features]
95: y_test_temp = df_test['temp_target']
96: y_test_rain = df_test['rain_target']
97:

98: clim = build_climatology(df_pre_test)
99: test_with_clim = pd.merge(df_test.reset_index(), clim, on=['target_month', 'target_hour'], how='left')
100:

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Created At: 2026-10-07T13:51:40+01:00
Completed At: 2026-10-07T13:51:40+01:00
File Path: file:///[REDACTED_PATH]/G00dS0ul-Hackerton/w1-go-out-today/src/w1_go_out_today/forecast.py
Total Lines: 276
Total Bytes: 12066
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101: base_mae_clim = mean_absolute_error(test_with_clim['temp_target'], test_with_clim['clim_temp'])
102: base_roc_clim = roc_auc_score(test_with_clim['rain_target'], test_with_clim['clim_rain_prob'])
103: base_brier_clim = brier_score_loss(test_with_clim['rain_target'], test_with_clim['clim_rain_prob'])
104:

105: pers_temp = test_with_clim['temperature_2m']
106: pers_rain_prob = (test_with_clim['precipitation'] >= 0.1).astype(int)
107:

108: base_mae_pers = mean_absolute_error(test_with_clim['temp_target'], pers_temp)
109: base_roc_pers = roc_auc_score(test_with_clim['rain_target'], pers_rain_prob)
110: base_brier_pers = brier_score_loss(test_with_clim['rain_target'], pers_rain_prob)
111:
112: if not quiet:
113: print(f"\n--- Baselines ---")
114: print(f"Climatology: Temp MAE={base_mae_clim:.3f}, Rain ROC={base_roc_clim:.3f}, Rain Brier={base_brier_clim:.3f}")
115: print(f"Persistence: Temp MAE={base_mae_pers:.3f}, Rain ROC={base_roc_pers:.3f}, Rain Brier={base_brier_pers:.3f}")
116:

117: print(f"\n--- TabPFN (n_estimators={n_estimators}) ---")
118:

119: t0 = time.time()
120: clf = TabPFNClassifier(n_estimators=n_estimators)
121: clf.fit(X_train, y_train_rain)
122: reg = TabPFNRegressor(n_estimators=n_estimators)
123: reg.fit(X_train, y_train_temp)
124: fit_time = time.time() - t0
125:

126: t0 = time.time()
127: rain_probs = clf.predict_proba(X_test)[:, 1] if len(clf.classes_) > 1 else np.zeros(len(X_test))
128: temp_preds = reg.predict(X_test)
129: pred_time = time.time() - t0
130:

131: roc = roc_auc_score(y_test_rain, rain_probs)
132: brier = brier_score_loss(y_test_rain, rain_probs)
133: mae = mean_absolute_error(y_test_temp, temp_preds)
134: total_time = fit_time + pred_time
135:

136: if not quiet:
137: print(f"Fit time: {fit_time:.2f}s | Predict time: {pred_time:.2f}s | Total: {total_time:.2f}s")
138: print(f"TabPFN overall: Temp MAE={mae:.3f}, Rain ROC={roc:.3f}, Rain Brier={brier:.3f}")
139:

140: results = test_with_clim.copy()
141: results['tabpfn_temp'] = temp_preds
142: results['tabpfn_rain'] = rain_probs
143:

144: for h in sorted(results['hours_ahead'].unique()):
145: sub = results[results['hours_ahead'] == h]
146: if len(sub) > 0 and len(sub['rain_target'].unique()) > 1:
147: h_mae = mean_absolute_error(sub['temp_target'], sub['tabpfn_temp'])
148: h_roc = roc_auc_score(sub['rain_target'], sub['tabpfn_rain'])
149: h_brier = brier_score_loss(sub['rain_target'], sub['tabpfn_rain'])
150: print(f" +{h}h: MAE={h_mae:.3f}, ROC={h_roc:.3f}, Brier={h_brier:.3f}")
151:
152: return clf, reg, features, mae, roc, brier, total_time
153:
154: def forecast(df, issue_time, n=12, clf=None, reg=None, features=None):
155: df_pairs = prepare_data(df, n=n, is_training=False)
156:

157: if clf is None or reg is None:
158: raise ValueError("Models not provided.")
159:

160: issue_data = df_pairs[df_pairs.index == pd.to_datetime(issue_time)].copy()
161: if len(issue_data) == 0:
162: raise ValueError(f"Issue time {issue_time} not found in prepared data.")
163:

164: issue_data = issue_data.sort_values('hours_ahead')
165:

166: X = issue_data[features]
167: rain_probs = clf.predict_proba(X)[:, 1]
168: temp_preds = reg.predict(X)
169:

170: res = []
171: for i, h in enumerate(issue_data['hours_ahead']):
172: target_time = pd.to_datetime(issue_time) + timedelta(hours=int(h))
173: res.append({
174: 'time': target_time.strftime('%H:%M'),
175: 'hours_ahead': h,
176: 'temp_pred': f"{temp_preds[i]:.1f}°C",
177: 'rain_prob': f"{int(round(rain_probs[i]*100))}%"
178: })
179: return pd.DataFrame(res)
180:
181: import sys
182: def run_forecast_evaluation():
183: csv_path = "data/lagos_weather.csv"
184: if not os.path.exists(csv_path):
185: print(f"CSV not found at {csv_path}")
186: return
187:

188: df = pd.read_csv(csv_path)
189: df_pairs = prepare_data(df, n=12, is_training=True)
190:

191: if "--sample-size-check" in sys.argv:
192: print("\n=== Sample Size Check (n_estimators=2) ===")
193: print(f"{'Rows':>6} | {'Temp MAE':>8} | {'Rain AUC':>8} | {'Rain Brier':>10} | {'Total Time':>10}")
194: print("-" * 55)
195: for size in [1000, 3000, 5000]:
196: df_train, df_test, df_pre_test = get_splits(df_pairs, train_size=size)
197: , _, _, mae, roc, brier, total_time = train_and_evaluate(df_train, df_test, df_pre_test, n_estimators=2, quiet=True)
198: print(f"{size:>6} | {mae:>8.3f} | {roc:>8.3f} | {brier:>10.3f} | {total_time:>9.2f}s")
199: return
200:

201: df_train, df_test, df_pre_test = get_splits(df_pairs, train_size=3000)
202:

203: print(f"Split Date: {df_test.index.min()} to {df_test.index.max()}")
204: print(f"Train rows: {len(df_train)}, Test rows: {len(df_test)}")
205:

206: t_start = time.time()
207: clf_def, reg_def, feats, _, _, _, _ = train_and_evaluate(df_train, df_test, df_pre_test, n_estimators=2)
208:

209: if "--compare" in sys.argv:
210: print("\n=== Running n_estimators=10 Comparison ===")
211: train_and_evaluate(df_train, df_test, df_pre_test, n_estimators=10)
212: t_total = time.time() - t_start
213: if t_total > 300:
214: print("\nWARNING: Run took > 5 minutes. Consider using the TabPFN API client fallback.")
215:

216: # Live forecast from latest 06:00
217: df['time'] = pd.to_datetime(df['time'])
218: latest_06 = df[df['time'].dt.hour == 6]['time'].max()
219:

220: # Or accept from args
221: for arg in sys.argv:
222: if arg.startswith("--issue-time="):
223: latest_06 = pd.to_datetime(arg.split("=")[1])
224:

225: print(f"\n--- Forecast for issue time ({latest_06}) ---")
226: try:
227: fcst = forecast(df, latest_06, n=12, clf=clf_def, reg=reg_def, features=feats)
228:

229: # Look up actuals
230: has_actuals = False
231: actual_temps = []
232: actual_rains = []
233: df_indexed = df.set_index('time')
234:

235: for h in fcst['hours_ahead']:
236: target_time = latest_06 + timedelta(hours=int(h))
237: if target_time in df_indexed.index:
238: row = df_indexed.loc[target_time]
239: if not pd.isna(row['temperature_2m']) and not pd.isna(row['precipitation']):
240: actual_temps.append(row['temperature_2m'])
241: actual_rains.append("Yes" if row['precipitation'] >= 0.1 else "No")
242: has_actuals = True
243: continue
244: actual_temps.append(None)
245: actual_rains.append(None)
246:

247: fcst['actual temp'] = [f"{t:.1f}°C" if t is not None else "-" for t in actual_temps]
248: fcst['actually rained?'] = [r if r is not None else "-" for r in actual_rains]
249:

250: fcst = fcst.rename(columns={'temp_pred': 'forecast temp', 'rain_prob': 'rain chance'})
251:

252: cols_to_print = ['time', 'forecast temp', 'actual temp', 'rain chance', 'actually rained?']
253: if not has_actuals:
254: cols_to_print = ['time', 'forecast temp', 'rain chance']
255:

256: print(fcst[cols_to_print].to_string(index=False))
257:

258: if has_actuals:
259: valid_idx = [i for i, t in enumerate(actual_temps) if t is not None]
260: raw_temp_preds = [float(t.replace('°C', '')) for t in fcst['forecast temp']]
261: raw_rain_probs = [int(p.replace('%', '')) for p in fcst['rain chance']]
262: errs = [abs(raw_temp_preds[i] - actual_temps[i]) for i in valid_idx]
263: avg_err = sum(errs) / len(errs) if errs else 0
264: rainy_hits = sum(1 for i in valid_idx if actual_rains[i] == "Yes" and raw_rain_probs[i] > 50)
265: rainy_total = sum(1 for i in valid_idx if actual_rains[i] == "Yes")
266: print(f"\nSummary for {latest_06.date()}: Avg temp error {avg_err:.1f}°C. Rainy hours predicted (>50% chance): {rainy_hits}/{rainy_total}.")
267:

268: except Exception as e:
269: import traceback
270: traceback.print_exc()
271: print(f"Forecast failed: {e}")
272:
273:
274: if __name_ == "main":
275: run_forecast_evaluation()
276:
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Created At: 2026-10-07T13:52:29+01:00
Completed At: 2026-10-07T13:52:29+01:00
File Path: file:///[REDACTED_PATH]/G00dS0ul-Hackerton/w1-go-out-today/tests/test_forecast.py
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1: import pytest
2: import pandas as pd
3: import numpy as np
4: from datetime import timedelta
5: import sys
6:
7: # Monkey patch threadpoolctl
8: import threadpoolctl
9: threadpoolctl.ThreadpoolController.find_libraries_on_windows = lambda self: None
10:
11: from w1_go_out_today.forecast import prepare_data, get_splits, build_climatology, train_and_evaluate, forecast
12: from tabpfn import TabPFNClassifier, TabPFNRegressor
13:
14: class StubTabPFNClassifier:
15: def __init(self, **kwargs):
16: self.classes = [0, 1]
17: def fit(self, X, y):
18: return self
19: def predict_proba(self, X):
20: return np.array([[0.5, 0.5] for _ in range(len(X))])
21:
22: class StubTabPFNRegressor:
23: def init(self, **kwargs):
24: pass
25: def fit(self, X, y):
26: return self
27: def predict(self, X):
28: return np.ones(len(X)) * 25.0
29:
30: @pytest.fixture
31: def mock_tabpfn(monkeypatch):
32: monkeypatch.setattr('w1_go_out_today.forecast.TabPFNClassifier', StubTabPFNClassifier)
33: monkeypatch.setattr('w1_go_out_today.forecast.TabPFNRegressor', StubTabPFNRegressor)
34:
35: def test_prepare_data_no_leakage():
36: times = pd.date_range("2023-01-01 00:00", periods=48, freq="h")
37: df = pd.DataFrame({
38: "time": times,
39: "temperature_2m": np.random.rand(48) * 10 + 20,
40: "relative_humidity_2m": np.random.rand(48),
41: "dew_point_2m": np.random.rand(48),
42: "precipitation": np.random.rand(48),
43: "cloud_cover": np.random.rand(48),
44: "pressure_msl": np.random.rand(48),
45: "wind_speed_10m": np.random.rand(48),
46: })
47:

48: df_pairs = prepare_data(df, n=3)
49:

50: row = df_pairs.iloc[0]
51: issue_time = row.name
52: h = row['hours_ahead']
53: expected_target_time = issue_time + timedelta(hours=h)
54:

55: expected_temp = df[df['time'] == expected_target_time]['temperature_2m'].values[0]
56: assert np.isclose(row['temp_target'], expected_temp)
57: assert 'rain_target' in df_pairs.columns
58:

59: def test_get_splits():
60: times = pd.date_range("2023-01-01 00:00", periods=400*24, freq="h")
61: df = pd.DataFrame({
62: "time": times,
63: "temperature_2m": np.random.rand(len(times)),
64: "relative_humidity_2m": np.random.rand(len(times)),
65: "dew_point_2m": np.random.rand(len(times)),
66: "precipitation": np.random.rand(len(times)),
67: "cloud_cover": np.random.rand(len(times)),
68: "pressure_msl": np.random.rand(len(times)),
69: "wind_speed_10m": np.random.rand(len(times)),
70: })
71: df_pairs = prepare_data(df, n=2)
72: df_train, df_test, df_pre_test = get_splits(df_pairs)
73:

74: max_time = df_pairs.index.max()
75: test_start = max_time - timedelta(days=30)
76:

77: assert df_test.index.min() >= test_start
78: assert all(df_test.index.hour == 6)
79:

80: assert df_train.index.max() < test_start
81: assert len(df_train) <= 3000
82:
83: def test_climatology():
84: times = pd.date_range("2023-01-01 00:00", periods=100, freq="h")
85: df = pd.DataFrame({
86: "target_month": times.month,
87: "target_hour": times.hour,
88: "temp_target": np.ones(100) * 20,
89: "rain_target": np.zeros(100)
90: })
91: clim = build_climatology(df)
92: assert 'clim_temp' in clim.columns
93: assert 'clim_rain_prob' in clim.columns
94:

95: def test_train_and_evaluate(mock_tabpfn):
96: times = pd.date_range("2023-01-01 00:00", periods=400*24, freq="h")
97: df = pd.DataFrame({
98: "time": times,
99: "temperature_2m": np.random.rand(len(times)),
100: "relative_humidity_2m": np.random.rand(len(times)),
101: "dew_point_2m": np.random.rand(len(times)),
102: "precipitation": np.random.rand(len(times)),
103: "cloud_cover": np.random.rand(len(times)),
104: "pressure_msl": np.random.rand(len(times)),
105: "wind_speed_10m": np.random.rand(len(times)),
106: })
107: df_pairs = prepare_data(df, n=2)
108: df_train, df_test, df_pre_test = get_splits(df_pairs)
109:

110: clf, reg, feats = train_and_evaluate(df_train, df_test, df_pre_test, n_estimators=2)
111: assert clf is not None
112: assert reg is not None
113: assert len(feats) > 0
114:
115: def test_forecast_latest_issue_time(mock_tabpfn):
116: times = pd.date_range("2023-01-01 00:00", periods=100, freq="h")
117: df = pd.DataFrame({
118: "time": times,
119: "temperature_2m": np.random.rand(len(times)),
120: "relative_humidity_2m": np.random.rand(len(times)),
121: "dew_point_2m": np.random.rand(len(times)),
122: "precipitation": np.random.rand(len(times)),
123: "cloud_cover": np.random.rand(len(times)),
124: "pressure_msl": np.random.rand(len(times)),
125: "wind_speed_10m": np.random.rand(len(times)),
126: })
127: latest_time = df['time'].max()
128:

129: # Need to get a stub model
130: clf = StubTabPFNClassifier()
131: reg = StubTabPFNRegressor()
132: feats = ['temperature_2m', 'relative_humidity_2m', 'hours_ahead'] # stub features
133:

134: # This currently crashes because prepare_data drops NA targets for the latest row
135: fcst = forecast(df, latest_time, n=3, clf=clf, reg=reg, features=feats)
136: assert len(fcst) == 3
137: def test_forecast_no_actuals_leakage(mock_tabpfn):
138: # Create 100 hours of history
139: times = pd.date_range("2023-01-01 00:00", periods=100, freq="h")
140: df = pd.DataFrame({
141: "time": times,
142: "temperature_2m": np.random.rand(100),
143: "relative_humidity_2m": np.random.rand(100),
144: "dew_point_2m": np.random.rand(100),
145: "precipitation": np.random.rand(100),
146: "cloud_cover": np.random.rand(100),
147: "pressure_msl": np.random.rand(100),
148: "wind_speed_10m": np.random.rand(100),
149: })
150:

151: # Issue time is the absolute last row. No future rows exist in the DataFrame.
152: latest_time = df['time'].max()
153:

154: clf = StubTabPFNClassifier()
155: reg = StubTabPFNRegressor()
156: feats = ['temperature_2m', 'relative_humidity_2m', 'hours_ahead']
157:

158: # If forecast() tries to look up targets at latest_time + h, it would fail or leak.
159: # Since the rows literally don't exist, success proves no future rows are required.
160: fcst = forecast(df, latest_time, n=12, clf=clf, reg=reg, features=feats)
161:

162: assert len(fcst) == 12
163: # Ensure it only outputs what we expect and no actuals
164: expected_cols = {'time', 'hours_ahead', 'temp_pred', 'rain_prob'}
165: assert set(fcst.columns) == expected_cols
166:
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Created At: 2026-10-07T13:52:29+01:00
Completed At: 2026-10-07T13:52:29+01:00
File Path: file:///[REDACTED_PATH]/G00dS0ul-Hackerton/w1-go-out-today/README.md
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1: # w1-go-out-today
2:
3: A local, voice-first morning brief that tells people in Lagos the best time today to be outside.
4: (work in progress)
5:
6: ## Setup
7:
8: This repository is managed with uv (Python 3.12), and includes pytest and ruff.
9:
10: ## Data Processing
11:
12: To download historical hourly weather data for Lagos from scratch, run:
13: bash
14: uv run python -m w1_go_out_today.fetch_weather
15:
16:
17: ### Data Source
18: Weather data is provided by the Open-Meteo Historical Weather API under the CC BY 4.0 license.
19:
20: ### Missing Data Handling
21: During the data fetch step, any hourly rows containing missing values (null) for any of the requested variables are dropped entirely.
22:
23: ## Changes after submission
24:
25: ## Forecast Evaluation
26:
27: To evaluate the TabPFN model (predicting rain probability and temperature per hour) and compare it against climatology and persistence baselines, run:
28: bash
29: uv run w1-go-out-today
30:
31:
32: To run the slower n_estimators=10 execution for comparison against the default n_estimators=2 output:
33: bash
34: uv run w1-go-out-today --compare
35:
36:
37: ### Metrics (Holdout: 2026-09-01 to 2026-09-30)
38:
39: | Model | Temp MAE | Rain ROC AUC | Rain Brier Score |
40: | --- | --- | --- | --- |
41: | TabPFN (n=2) | 0.712 | 0.733 | 0.205 |
42: | Climatology | 0.750 | 0.606 | 0.231 |
43: | Persistence | 1.608 | 0.663 | 0.322 |
44:
45: (Note: Per-lead hourly numbers output by the CLI come from exactly 30 rows each (one 06:00 issue time per day for 30 days) so they can be quite noisy).
46:
47: Note on TabPFN:
48: The forecast uses TabPFN running locally on the CPU. The source code is licensed under the Apache License 2.0. The weights are TabPFN 3.5, which use a non-commercial licence (see the TabPFN License File).
49: On Windows, set TABPFN_TOKEN in .env. The browser login prompt fails.
50:
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Created At: 2026-10-07T13:52:31+01:00
Completed At: 2026-10-07T13:52:31+01:00
File Path: file:///[REDACTED_PATH]/G00dS0ul-Hackerton/w1-go-out-today/src/w1_go_out_today/__init__.py
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1: from .forecast import run_forecast_evaluation
2:
3: def main() -> None:
4: run_forecast_evaluation()
5:
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Created At: 2026-10-07T13:52:34+01:00
Completed At: 2026-10-07T13:52:34+01:00
File Path: file:///[REDACTED_PATH]/G00dS0ul-Hackerton/memory/patterns.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: # Patterns
2:
3: Conventions to follow. Stated preferences and non-obvious discoveries.
4:
5: ### P-1 - Only Ise marks work done
6: - Rule: Never set a task, milestone or plan to Done in any file, tracker or summary. You may report "IMPLEMENTED - AWAITING ISE CONFIRMATION". If a status looks wrong, flag it; do not fix it.
7: - Applies when: finishing any milestone, updating a plan status, summarising state, writing a journal checkpoint.
8: - Example: plans/w1-M1-repo-setup.md section 14 lists what Ise checks; the status stays IMPLEMENTED - AWAITING ISE CONFIRMATION.
9: - Date: 2026-10-05
10:
11: ### P-2 - Antigravity session upload to DEV goes through DevRelay curated submit
12: - Rule: To save an Antigravity session to DEV, read the transcript at %USERPROFILE%.gemini\antigravity\brain<conversation-id>.system_generated\logs\transcript_full.jsonl, try the presigned raw upload, and if it returns 403 submit curated data with devrelay-gateway submit_agent_session. Sessions stay private. DEV labels these sessions "Gemini CLI"; that is expected.
13: - Applies when: saving a session to DEV, closing a milestone, uploading an agent transcript.
14: - Example: Test upload on 2026-10-05 - presign_agent_session_upload then PUT returned 403; submit_agent_session succeeded and the private session opened on DEV.
15: - Date: 2026-10-05
16:
17: ### P-3 - Windows paths contain an emoji username
18: - Rule: Never type my Windows home path by hand. Use %USERPROFILE% or $env:USERPROFILE, or the 8.3 short path, in commands and config. Write files with the file-write tool, not shell redirection.
19: - Applies when: writing a path into a command, a config file, a script, or an MCP config entry.
20: - Example: devrelay-gateway was registered in %USERPROFILE%.gemini\config\mcp_config.json using $env:USERPROFILE so the emoji was never typed.
21: - Date: 2026-10-05
22:
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Created At: 2026-10-07T13:52:34+01:00
Completed At: 2026-10-07T13:52:34+01:00
File Path: file:///[REDACTED_PATH]/G00dS0ul-Hackerton/memory/corrections.md
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1: # Corrections
2:
3: Mistakes made once. Scan Trigger: lines before every task.
4:
5: Empty by design. Corrections are earned, not seeded. An entry copied from
6: another project describes a mistake that was never made here, and it will be
7: cited as though it were.
8:
9: ---
10:
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Created At: 2026-10-07T13:52:36+01:00
Completed At: 2026-10-07T13:52:36+01:00
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1: # Week 1 rules - Hacktoberfest Open-Source AI Challenge: Touch Grass
2:
3: Source: https://dev.to/challenges/hacktoberfest-week1-2026-10-05
4: Copied: 2026-10-05
5:
6: ## Prompt
7:
8: Build something with open-source AI at its core: an open-weight model, an
9: open-source agent harness or framework, local inference, or all three. The
10: open pieces must be what makes the project work.
11:
12: The post must say why open innovation matters for what I built.
13:
14: ## Theme: Touch Grass
15:
16: Build something with open-weight models or open-source AI that gets people
17: off the screen and into the world. The best builds make the screen the
18: shortest part of the experience. Bonus: take it outside, use it, and write
19: about how it went.
20:
21: ## Dates
22:
23: - Start: 2026-10-05
24: - Submissions due: 2026-10-11 (I confirm the exact cut-off time in the
25: official rules; my target is 2026-10-11 10:00 Lagos)
26: - Winners: week of 2026-10-12
27:
28: ## Rules that bind the build
29:
30: - New project. The repo is started and completed inside the window.
31: - Commits after the deadline must be noted in the README.
32: - Reused open source code is allowed if my changes are significant and it is
33: credited.
34: - One submission per challenge. It can win once, but is entered in every
35: category it genuinely qualifies for.
36: - Post in English, using the submission template, with the required tags
37: devchallenge and hf26challenge.
38:
39: ## Judging
40:
41: 1. Writing Quality (weighted most heavily)
42: 2. Relevance to the Prompt and Theme
43: 3. Creativity
44: 4. Technical Execution
45: 5. Use of Partner Technology (optional)
46:
47: ## Prize categories I am targeting
48:
49: - Best Use of TabPFN ($200, featured): use TabPFN to forecast, predict,
50: classify or spot anomalies from historical data like a CSV.
51: - Best Use of ElevenLabs ($100): give an open-source agent a voice,
52: transcribe audio for a local model, or narrate the demo.
53: - Optional, only if genuinely used: Best Use of Entire (share agent
54: sessions), Best Use of GitHub Copilot.
55:
56: Not targeting Gemma. The model must not be Gemma (see D-3).
57:
58: ## Submission template sections
59:
60: What I Built / Demo / Code / How I Built It / Why Does Open Innovation
61: Matter? / My Agent Session / Prize Categories
62:
The above content shows the entire, complete file contents of the requested file.

Created At: 2026-10-07T13:52:44+01:00
Completed At: 2026-10-07T13:52:47+01:00

The command exited with code 0.
Output:
Package Version Editable project location

annotated-types 0.8.0
anyio 4.15.1
certifi 2026.7.22
charset-normalizer 3.5.2
click 8.5.0
cloudpickle 3.1.2
colorama 0.4.6
contourpy 1.4.0
cycler 0.12.1
einops 0.8.2
filelock 4.0.12
fonttools 4.66.1
fsspec 2026.9.0
h11 0.16.0
hf-xet 1.6.0
httpcore2 2.13.1
httpx2 2.13.1
huggingface-hub 2.1.1
idna 3.20
iniconfig 2.3.0
jinja2 3.1.6
joblib 1.6.0
kiwisolver 1.5.1
lightgbm 4.7.0
markupsafe 3.0.4
matplotlib 3.11.2
mpmath 1.3.0
narwhals 2.26.0
networkx 3.7
numpy 2.5.3
packaging 26.3
pandas 3.0.6
pillow 12.3.0
pluggy 1.6.0
pydantic 2.13.5
pydantic-core 2.46.5
pydantic-settings 2.15.0
pydot 4.0.1
pygments 2.21.0
pyparsing 3.3.3
pytest 9.1.1
python-dateutil 2.9.0.post0
python-dotenv 1.2.4
pyyaml 6.0.3
requests 2.34.2
ruff 0.16.10
safetensors 0.8.0
scikit-learn 1.9.1
scipy 1.18.1
setuptools 84.0.0
six 1.17.0
skrub 0.10.1
sympy 1.14.0
tabpfn 9.1.0
tabulate 0.10.0
threadpoolctl 3.7.0
torch 2.14.1+cpu
tqdm 4.70.1
truststore 0.10.4
typing-extensions 4.16.0
typing-inspection 0.4.4
tzdata 2026.5
urllib3 2.8.0
w1-go-out-today 0.1.0 [REDACTED_PATH]\G00dS0ul-Hackerton\w1-go-out-today

Created At: 2026-10-07T13:52:52+01:00
Completed At: 2026-10-07T13:52:52+01:00
File Path: file:///[REDACTED_PATH]/G00dS0ul-Hackerton/plans/w1-M1-set-up-repo.md
Total Lines: 130
Total Bytes: 4929
Showing lines 1 to 130
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: # Set up the repo
2:
3: - Task: w1-M1
4: - Status: IMPLEMENTED - AWAITING ISE CONFIRMATION
5: - Created: 2026-10-06
6: - Updated: 2026-10-06
7: - Repo: w1-go-out-today
8:
9: ## 1. Task
10:
11: Set up w1-go-out-today as a uv Python 3.12 project with pytest, ruff, python-dotenv, a .env.example, a .gitignore, an MIT LICENSE and a README that only describes what exists, plus recommend the Ollama model.
12:
13: ## 2. Memory that applies
14:
15: - D-1 - Python 3.12 with uv, pytest, ruff and python-dotenv
16: - D-2 - One new repo per challenge, in the G00dS0ul-Hackerton org
17: - D-3 - Week 1 model is open-weight and not Gemma (Qwen or Llama family)
18:
19: ## 3. Files affected
20:
21: | Repo | Path | Action | Notes |
22: | --- | --- | --- | --- |
23: | w1-go-out-today | pyproject.toml | create | via uv init or manual |
24: | w1-go-out-today | .env | create | |
25: | w1-go-out-today | .env.example | create | |
26: | w1-go-out-today | .gitignore | create | |
27: | w1-go-out-today | LICENSE | create | MIT License |
28: | w1-go-out-today | README.md | modify | Update to reflect current setup |
29: | w1-go-out-today | tests/test_smoke.py | create | For uv run pytest |
30:
31: ## 4. Interfaces
32:
33: | Interface | Status | Evidence |
34: | --- | --- | --- |
35: | uv | confirmed in installed package | (Assuming uv is available globally per D-1) |
36:
37: ## 5. Scope check
38:
39: Milestone M1 from docs/challenge/w1/build-plan.md. Acceptance criteria, quoted verbatim:
40:
41: > - uv run pytest passes with at least one smoke test.
42: > - uv run ruff check reports no errors.
43: > - .env is listed in .gitignore and git status does not show it.
44: > - .env.example lists every key name with an empty value.
45: > - The README describes only what exists, and has an empty
46: > "Changes after submission" section.
47: > - The chosen Ollama model is recorded as a decision in memory.
48:
49: ## 6. Steps
50:
51: 1. Run uv init inside the w1-go-out-today repo.
52: 2. Run uv add --dev pytest ruff and uv add python-dotenv.
53: 3. Create tests/test_smoke.py with a simple test def test_smoke(): assert True.
54: 4. Run uv run pytest to verify the smoke test passes.
55: 5. Create .env and .env.example with standard keys for the project (e.g. ELEVENLABS_API_KEY=).
56: 6. Create .gitignore and ensure .env and .venv are ignored.
57: 7. Run uv run ruff check to ensure no errors exist.
58: 8. Create an MIT LICENSE file.
59: 9. Update README.md to describe only what exists so far, adding an empty "Changes after submission" section.
60: 10. Update memory/decisions.md to record the chosen Ollama model (Qwen 2.5 7B, qwen2.5:7b).
61:
62: ## 7. Time cost
63:
64: ~0.5 h. Fits M1.
65:
66: ## 8. Objections
67:
68: ### O-1 Model selection - minor
69:
70: Objection: The decision memory entry for the model must be confirmed with the user.
71: Resolution: Accepted. The user has explicitly proposed qwen2.5:7b. I have validated it. We will formally write this to memory/decisions.md once the plan is approved.
72:
73: ### O-2 uv init behaviour - minor
74:
75: Objection: uv init might create additional boilerplate files (like hello.py) or overwrite README.md.
76: Resolution: We will carefully check what uv init generates and clean up any boilerplate hello.py or main.py files, and ensure the README matches the strict acceptance criteria.
77:
78: ### O-3 Missing .env keys - minor
79:
80: Objection: At this stage, we might not know all .env keys required for future milestones.
81: Resolution: Accepted. We know ElevenLabs is required from the build plan pipeline (ElevenLabs (audio)). We will add ELEVENLABS_API_KEY= to .env.example as a starting point. More can be added later.
82:
83: ## 9. Verdict
84:
85: READY FOR APPROVAL - Awaiting approval. I will not write code until you reply APPROVE.
86:
87: ## 10. Approval
88:
89: - Approved: 2026-10-06, "Approve"
90: - Changes requested: None
91: - Confirmed: 2026-10-06, "Okay can you log this in the journal entry and save this agent session"
92:
93: ## 11. Implementation log
94:
95: - [x] Step 1
96: - [x] Step 2
97: - [x] Step 3
98: - [x] Step 4
99: - [x] Step 5
100: - [x] Step 6
101: - [x] Step 7
102: - [x] Step 8
103: - [x] Step 9
104: - [x] Step 10
105:
106: ## 12. Deviations
107:
108: | Expected | Found | Did | Why the plan missed it |
109: | --- | --- | --- | --- |
110: | uv available globally in PATH | uv not recognized | Installed uv via official install script to ~/.local/bin and used full path | Assumed D-1 implied uv was already installed in PATH |
111: | README.md overwritten by uv init | README.md unchanged by uv init | Replaced contents with required criteria | Expected uv init to be more destructive to existing files |
112:
113: ## 13. Memory written
114:
115: - D-4 - Week 1 specific model is Qwen 2.5 7B
116:
117: ## 14. For Ise to confirm
118:
119: - uv run pytest passes
120: - uv run ruff check reports no errors
121: - .env is ignored by git
122: - .env.example is present
123: - README is correct
124: - Memory contains the model decision
125:
126: ## 15. Journal + session
127:
128: - Journal entry: journal/w1-go-out-today.md - Checkpoint 1
129: - Agent session: https://dev.to/agent_sessions/w1-m1-set-up-the-repo-updated-plltsz (private)
130:
The above content shows the entire, complete file contents of the requested file.

Created At: 2026-10-07T13:53:12+01:00
Completed At: 2026-10-07T13:53:15+01:00

The command exited with code 0.
Output:
{
"models": [
{
"name": "qwen2.5:7b",
"model": "qwen2.5:7b",
"modified_at": "2026-10-06T14:17:14.8295644+01:00",
"size": 4683087332,
"digest": "845dbda0ea48ed749caafd9e6037047aa19acfcfd82e704d7ca97d631a0b697e",
"details": "@{parent_model=; format=gguf; family=qwen2; families=System.Object[]; parameter_size=7.6B; quantization_level=Q4_K_M; context_length=32768; embedding_length=3584; runner=ggml}",
"capabilities": "completion tools"
},
{
"name": "qwen2.5-coder:3b",
"model": "qwen2.5-coder:3b",
"modified_at": "2026-10-03T07:16:27.3721203+01:00",
"size": 1929912626,
"digest": "f72c60cabf6237b07f6e632b2c48d533cef25eda2efbd34bed21c5e9c01e6225",
"details": "@{parent_model=; format=gguf; family=qwen2; families=System.Object[]; parameter_size=3.1B; quantization_level=Q4_K_M; context_length=32768; embedding_length=2048; runner=ggml}",
"capabilities": "completion tools insert"
},
{
"name": "gemma3:4b",
"model": "gemma3:4b",
"modified_at": "2026-10-03T06:50:36.3371027+01:00",
"size": 3338801804,
"digest": "a2af6cc3eb7fa8be8504abaf9b04e88f17a119ec3f04a3addf55f92841195f5a",
"details": "@{parent_model=; format=gguf; family=gemma3; families=System.Object[]; parameter_size=4.3B; quantization_level=Q4_K_M; context_length=131072; embedding_length=2560; runner=ggml}",
"capabilities": "completion vision"
},
{
"name": "qwen2.5-coder:7b",
"model": "qwen2.5-coder:7b",
"modified_at": "2026-10-03T06:39:29.9443429+01:00",
"size": 4683087561,
"digest": "dae161e27b0e90dd1856c8bb3209201fd6736d8eb66298e75ed87571486f4364",
"details": "@{parent_model=; format=gguf; family=qwen2; families=System.Object[]; parameter_size=7.6B; quantization_level=Q4_K_M; context_length=32768; embedding_length=3584; runner=ggml}",
"capabilities": "completion tools insert"
}
]
}

Created At: 2026-10-07T13:53:18+01:00
Tool is running as a background task with task id: e9cc7112-54e0-4d02-b5fd-28eee49d8c50/task-58
Task Description: $body = @{ model = "qwen2.5:7b"; prompt = "Say hello in 5 words."; stream = $false } | ConvertTo-Json; Invoke-RestMethod -Uri "http://[REDACTED]:11434/api/generate" -Method Post -Body $body -ContentType "application/json" | Select-Object model, response, done
Task logs are available at: file:///[REDACTED_PATH]/.gemini/antigravity/brain/e9cc7112-54e0-4d02-b5fd-28eee49d8c50/.system_generated/tasks/task-58.log
YOU MUST TAKE ONE OF THE FOLLOWING TWO ACTIONS: A) either proceed to other relevant work (if any) or, B) simply update the user with a short message (that you have launched the command and will wait for it to finish) and end the turn.
DO NOTHING ELSE.

Created At: 2026-10-07T13:53:45+01:00
Completed At: 2026-10-07T13:53:45+01:00
File Path: file:///[REDACTED_PATH]/G00dS0ul-Hackerton/plans/README.md
Total Lines: 116
Total Bytes: 2650
Showing lines 1 to 116
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: # Plans
2:
3: One file per planned task, produced by .agents/rules/plan-before-build.md.
4: They record what was planned, what was objected to, what I approved, and where
5: reality diverged. A later session must be able to pick up an approved plan and
6: continue without re-deriving it.
7:
8: ## Naming
9:
10: - w1-M3-tabpfn-forecast.md - milestone work (week + milestone id)
11: - w1-fix-empty-csv-crash.md - a fix with no milestone
12:
13: One task per file. Never overwrite an existing plan file.
14:
15: ## Status
16:
17: DRAFT -> AWAITING APPROVAL -> APPROVED -> IN PROGRESS ->
18: IMPLEMENTED - AWAITING ISE CONFIRMATION
19:
20: Also valid: BLOCKED, ABANDONED, SUPERSEDED BY <path>
21:
22: There is no DONE status. Only I confirm completion.
23:
24: ## Template
25:
26: markdown
27: # <Title>
28:
29: - **Task:** w1-M<n> or Fix: <one line>
30: - **Status:** DRAFT
31: - **Created:** YYYY-MM-DD
32: - **Updated:** YYYY-MM-DD
33: - **Repo:** w1-go-out-today
34:
35: ## 1. Task
36:
37: One sentence, as understood.
38:
39: ## 2. Memory that applies
40:
41: - C-n / D-n / P-n - what it requires here
42:
43: ## 3. Files affected
44:
45: | Repo | Path | Action | Notes |
46: | --- | --- | --- | --- |
47: | w1-go-out-today | `src/forecast.py` | create | |
48: | w1-go-out-today | `src/brief.py:41` | modify | |
49:
50: ## 4. Interfaces
51:
52: | Interface | Status | Evidence |
53: | --- | --- | --- |
54: | `tabpfn.TabPFNClassifier.fit/predict_proba` | confirmed in installed package | `.venv/.../tabpfn/classifier.py:<line>` |
55: | ElevenLabs text-to-speech | confirmed in official docs | <url> |
56: | Ollama chat | NOT CONFIRMED | stop |
57:
58: ## 5. Scope check
59:
60: Milestone M<n> from `docs/challenge/w1/build-plan.md`. Acceptance criteria,
61: quoted verbatim:
62:
63: > ...
64:
65: ## 6. Steps
66:
67: 1. What changes, which files, how verified (command).
68:
69: ## 7. Time cost
70:
71: ~<n> h. Fits / pushes M<n>.
72:
73: ## 8. Objections
74:
75: ### O-1 <title> - blocker | serious | minor
76:
77: **Objection:** ...
78: **Resolution:** amended the plan / accepted because ...
79:
80: (minimum three, or two for a short-form plan)
81:
82: ## 9. Verdict
83:
84: BLOCKED - numbered questions
85: or
86: READY FOR APPROVAL - Awaiting approval. I will not write code until you reply APPROVE.
87:
88: ## 10. Approval
89:
90: - **Approved:** YYYY-MM-DD, Ise's exact words
91: - **Changes requested:** ...
92:
93: ## 11. Implementation log
94:
95: - [x] Step 1 - `w1-go-out-today/src/forecast.py:12`
96: - [ ] Step 2
97:
98: ## 12. Deviations
99:
100: | Expected | Found | Did | Why the plan missed it |
101: | --- | --- | --- | --- |
102:
103: ## 13. Memory written
104:
105: - C-n / D-n / P-n - one line each, or "none"
106:
107: ## 14. For Ise to confirm
108:
109: - What to check, against the criteria in section 5.
110:
111: ## 15. Journal + session
112:
113: - Journal entry: `journal/w1-go-out-today.md` - <checkpoint title>, or "not yet"
114: - Agent session: <dev.to link> (private), or "not saved - <reason>"
115:
116:
The above content shows the entire, complete file contents of the requested file.

Created At: 2026-10-07T13:53:47+01:00
Completed At: 2026-10-07T13:53:53+01:00

The command exited with code 0.
Output:

Directory: [REDACTED_PATH]\G00dS0ul-Hackerton\plans

Mode LastWriteTime Length Name

-a---- 10/5/2026 10:49 PM 2650 README.md

-a---- 10/6/2026 5:00 PM 2910 w1-fix-tabpfn-win-auth.md

-a---- 10/6/2026 3:04 PM 4929 w1-M1-set-up-repo.md

-a---- 10/6/2026 3:52 PM 6483 w1-M2-lagos-weather.md

-a---- 10/6/2026 5:52 PM 9956 w1-M3-tabpfn-forecast.md

Created At: 2026-10-07T13:53:55+01:00
Completed At: 2026-10-07T13:53:55+01:00
You have 1 background task(s) currently running:
{
"taskId": "e9cc7112-54e0-4d02-b5fd-28eee49d8c50/task-58",
"toolName": "run_command",
"toolSummary": "Test Ollama /api/generate",
"description": "$body = @{ model = \"qwen2.5:7b\"; prompt = \"Say hello in 5 words.\"; stream = $false } | ConvertTo-Json; Invoke-RestMethod -Uri \"http://[REDACTED]:11434/api/generate\" -Method Post -Body $body -ContentType \"application/json\" | Select-Object model, response, done",
"startTime": "2026-10-07T12:53:18.000354500Z",
"stepIndex": 58,
"logUri": "file:///[REDACTED_PATH]/.gemini/antigravity/brain/e9cc7112-54e0-4d02-b5fd-28eee49d8c50/.system_generated/tasks/task-58.log"
}

Created At: 2026-10-07T13:54:03+01:00
Tool is running as a background task with task id: e9cc7112-54e0-4d02-b5fd-28eee49d8c50/task-76
Task Description: uv run python -c "import urllib.request, json, time; t0 = time.time(); req = urllib.request.Request('http://[REDACTED]:11434/api/generate', data=json.dumps({'model': 'qwen2.5:7b', 'prompt': 'Say hello in 5 words', 'stream': False}).encode('utf-8'), headers={'Content-Type': 'application/json'}); resp = urllib.request.urlopen(req); res = json.loads(resp.read()); print(f'Time: {time.time()-t0:.2f}s'); print(res.get('response'))"
Task logs are available at: file:///[REDACTED_PATH]/.gemini/antigravity/brain/e9cc7112-54e0-4d02-b5fd-28eee49d8c50/.system_generated/tasks/task-76.log
YOU MUST TAKE ONE OF THE FOLLOWING TWO ACTIONS: A) either proceed to other relevant work (if any) or, B) simply update the user with a short message (that you have launched the command and will wait for it to finish) and end the turn.
DO NOTHING ELSE.

Created At: 2026-10-07T13:54:19+01:00
Completed At: 2026-10-07T13:54:19+01:00
File Path: file:///[REDACTED_PATH]/.gemini/antigravity/brain/e9cc7112-54e0-4d02-b5fd-28eee49d8c50/.system_generated/tasks/task-76.log
Total Lines: 5
Total Bytes: 265
Showing lines 1 to 5
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: Building w1-go-out-today @ file:///[REDACTED_PATH]/G00dS0ul-Hackerton/w1-go-out-today
2: Built w1-go-out-today @ file:///[REDACTED_PATH]/G00dS0ul-Hackerton/w1-go-out-today
3: Uninstalled 1 package in 12ms
4: Installed 1 package in 258ms
5:
The above content shows the entire, complete file contents of the requested file.

plans/w1-M4-ollama-plan.md

Verdict: READY FOR APPROVAL — Awaiting approval. I will not write code until you reply APPROVE.

Objections and resolutions:
- O-1 Dependency footprint (minor): Using external client packages adds dependencies; resolved by using Python standard library urllib.request and json with zero new packages.
- O-2 Traceback leakage on connection or model failure (blocker): Network/HTTP failures from Ollama leaking stack traces; resolved by wrapping all network calls to exit with clear one-line guidance (Start Ollama and run ollama pull ...).
- O-3 Model hallucination or asserting rain certainty (serious): LLMs inventing numbers or asserting rain certainty; resolved by strict prompt negative constraints in prompts/plan.txt enforcing only tabular numbers and "chance of rain", never "it will rain".

Memory: no updates
Plan: plans/w1-M4-ollama-plan.md - AWAITING APPROVAL

plans/w1-M4-ollama-plan.md

Verdict: READY FOR APPROVAL — Awaiting approval. I will not write code until you reply APPROVE.

Objections and resolutions:
- O-1 Number verification false positives and decimal edge cases (serious): Decimal numbers (28.5°C) and time strings (07:00) risking false guard rejections; resolved by using decimal-aware regex (\b\d+(?:\.\d+)?\b), negative lookbehinds in sentence splitting, and extracting integer/hour equivalents from forecast rows into valid_numbers.
- O-2 Guard retry latency on slow CPU (minor): Retrying Ollama on CPU adding delay; resolved by capping retries at strictly 1 retry before immediately dropping to the deterministic fallback template.
- O-3 Backward compatibility of M3 CLI invocation (minor): Default uv run w1-go-out-today changing behavior; resolved by creating cli.py and gating full evaluation metrics behind --eval [--compare] [--sample-size-check] as requested.

Memory: no updates
Plan: plans/w1-M4-ollama-plan.md - AWAITING APPROVAL

Created At: 2026-10-07T13:56:22+01:00
Completed At: 2026-10-07T13:56:22+01:00
The following changes were made by the replace_file_content tool to: [REDACTED_PATH]\G00dS0ul-Hackerton\plans\w1-M4-ollama-plan.md. If relevant, proactively run terminal commands to execute this code for the USER. Don't ask for permission.
[diff_block_start]
@@ -1,7 +1,7 @@
# M4 Local plan text with Ollama

• Task: w1-M4 -- Status: AWAITING APPROVAL +- Status: IN PROGRESS
• Created: 2026-10-07
• Updated: 2026-10-07
• Repo: w1-go-out-today [diff_block_end]

Please note that the above snippet only shows the MODIFIED lines from the last change. It shows up to 3 lines of unchanged lines before and after the modified lines. The actual file contents may have many more lines not shown.

Created At: 2026-10-07T13:56:25+01:00
Completed At: 2026-10-07T13:56:25+01:00
The following changes were made by the replace_file_content tool to: [REDACTED_PATH]\G00dS0ul-Hackerton\plans\w1-M4-ollama-plan.md. If relevant, proactively run terminal commands to execute this code for the USER. Don't ask for permission.
[diff_block_start]
@@ -160,8 +160,8 @@

## 10. Approval

-- Approved: pending
-- Changes requested: Incorporated reviews: heat defined as ≥32°C, default "a walk", output guard (numbers, no "will rain", 2-3 sentences) with 1 retry and fallback template, CLI separated into cli.py with --eval and --issue-time, Ollama options (timeout 120s, temp 0.3, seed 42, keep_alive "1m"), monkeypatch offline tests, model comparison script in scripts/compare_models.py outputting to docs/challenge/w1/m4-model-comparison.md, and updating D-5.
+- Approved: 2026-10-07, "Approve"
+- Changes requested: None (incorporated prior to approval)

## 11. Implementation log

[diff_block_end]

Please note that the above snippet only shows the MODIFIED lines from the last change. It shows up to 3 lines of unchanged lines before and after the modified lines. The actual file contents may have many more lines not shown.

Created At: 2026-10-07T13:57:51+01:00
Tool is running as a background task with task id: e9cc7112-54e0-4d02-b5fd-28eee49d8c50/task-114
Task Description: uv run python -c "import pandas as pd; from w1_go_out_today.forecast import prepare_data, get_splits, train_and_evaluate, forecast; df = pd.read_csv('data/lagos_weather.csv'); df_pairs = prepare_data(df, n=12, is_training=True); df_train, df_test, df_pre_test = get_splits(df_pairs, train_size=3000); clf, reg, feats, _, _, _, _ = train_and_evaluate(df_train, df_test, df_pre_test, n_estimators=2, quiet=True); df['time'] = pd.to_datetime(df['time']); latest_06 = df[df['time'].dt.hour == 6]['time'].max(); fcst = forecast(df, latest_06, n=12, clf=clf, reg=reg, features=feats); print(fcst)"
Task logs are available at: file:///[REDACTED_PATH]/.gemini/antigravity/brain/e9cc7112-54e0-4d02-b5fd-28eee49d8c50/.system_generated/tasks/task-114.log
YOU MUST TAKE ONE OF THE FOLLOWING TWO ACTIONS: A) either proceed to other relevant work (if any) or, B) simply update the user with a short message (that you have launched the command and will wait for it to finish) and end the turn.
DO NOTHING ELSE.

Created At: 2026-10-07T13:59:11+01:00
Completed At: 2026-10-07T13:59:11+01:00
The following changes were made by the replace_file_content tool to: [REDACTED_PATH]\G00dS0ul-Hackerton\w1-go-out-today\src\w1_go_out_today\plan.py. If relevant, proactively run terminal commands to execute this code for the USER. Don't ask for permission.
[diff_block_start]
@@ -211,26 +211,22 @@
result = json.loads(response.read().decode("utf-8"))
return result.get("response", "").strip()

• except urllib.error.HTTPError as e:
• if e.code == 404:
• sys.stderr.write(f"Error: Model '{model}' is not pulled. Run: ollama pull {model}\n")
• sys.exit(1)
• else:
• except (TimeoutError, urllib.error.URLError) as e:
• if isinstance(e, urllib.error.HTTPError):
• if e.code == 404:
• sys.stderr.write(f"Error: Model '{model}' is not pulled. Run: ollama pull {model}\n")
• sys.exit(1) sys.stderr.write(f"Error: Ollama returned HTTP error {e.code}: {e.reason}\n") sys.exit(1) -
• except urllib.error.URLError:
• sys.stderr.write(
• f"Error: Ollama is not running. Start Ollama and run: ollama pull {model}\n"
• )

- sys.exit(1)

• except (TimeoutError, TimeoutException) if "TimeoutException" in globals() else TimeoutError:
• sys.stderr.write(f"Error: Ollama request timed out after {timeout}s.\n")

- sys.exit(1)

• elif isinstance(e, TimeoutError) or "timed out" in str(e).lower():
• sys.stderr.write(f"Error: Ollama request timed out after {timeout}s.\n")
• sys.exit(1)
• else:
• sys.stderr.write(
• f"Error: Ollama is not running. Start Ollama and run: ollama pull {model}\n"
• )
• sys.exit(1) except Exception as e:
• # Check if wrapped error is timeout if "timed out" in str(e).lower(): sys.stderr.write(f"Error: Ollama request timed out after {timeout}s.\n") sys.exit(1) [diff_block_end]

Please note that the above snippet only shows the MODIFIED lines from the last change. It shows up to 3 lines of unchanged lines before and after the modified lines. The actual file contents may have many more lines not shown.

Created At: 2026-10-07T13:59:15+01:00
Completed At: 2026-10-07T13:59:15+01:00
Task: e9cc7112-54e0-4d02-b5fd-28eee49d8c50/task-114
Status: RUNNING
Log: [REDACTED_PATH].gemini\antigravity\brain\e9cc7112-54e0-4d02-b5fd-28eee49d8c50.system_generated\tasks\task-114.log
Log output:
[REDACTED_PATH]\U0001f921\G00dS0ul-Hackerton\w1-go-out-today.venv\Lib\site-packages\tabpfn\validation.py:234: UserWarning: Running on CPU with more than 1000 samples may be slow.
Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client
_validate_num_samples_for_cpu(
[REDACTED_PATH]\U0001f921\G00dS0ul-Hackerton\w1-go-out-today.venv\Lib\site-packages\tabpfn\validation.py:234: UserWarning: Running on CPU with more than 1000 samples may be slow.
Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client
_validate_num_samples_for_cpu(

Last progress: 14s ago