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Jonathan Andrei
04 / Build journals

Stories from the build.

What was hard, what broke, what survived the cut, one post per project. Sorted by the recency of the work, not the day I wrote it down.

Still Here: Everyone Believes Black Dogs Get Left Behind. I Checked 99,916 Shelter Records. They Don't.
Aug. 2026
10 min read

Still Here: Everyone Believes Black Dogs Get Left Behind. I Checked 99,916 Shelter Records. They Don't.

510 dogs have an intake record at the Austin animal shelter and no outcome record. Still Here is the board, ordered by how long they have waited. The one at the top is Pancho, 449 days. Then I went looking for the reason the dogs at the top are the ones at the top, and the answer surprised me: black dog syndrome does not appear in 99,916 completed stays — the colors that wait are the coats bully-type dogs come in. Hold breed constant and coat moves the median by at most 7.5 days; change the breed and it moves by 20.

WeekendChallengeDogDaysSnowflake
Assay: A Skincare Tracker with an Error Bar. Cropping the Same Photo Moves Texture by 5.81 Points, So a Verdict Only Fires When the Change Beats That Floor on Your Face.
Aug. 2026
11 min read

Assay: A Skincare Tracker with an Error Bar. Cropping the Same Photo Moves Texture by 5.81 Points, So a Verdict Only Fires When the Change Beats That Floor on Your Face.

The measurement exists: YouCam's Skin Analysis API scores sixteen skin outputs from a photograph, and it is a genuinely good instrument (byte-identical input gives byte-identical output). The problem is that a score reported without its error is a number you cannot make a decision with. On my own face with one variable changed at a time, brightness ±8% moves blemishes by 4.85 points and cropping the same photograph differently moves texture by 5.81. A realistic four-week treatment effect is about five. Assay measures its own error first, then calls a change real only when it beats that floor.

YouCamHackathonYouCam Skin Analysis APIPerfect Corp
Unsay: An AI Medication-Safety Agent that Goes Back and Un-Says What It Told You. When the FDA Escalates a Recall, It Corrects Every Named Patient It Already Reassured, in Seconds.
Aug. 2026
13 min read

Unsay: An AI Medication-Safety Agent that Goes Back and Un-Says What It Told You. When the FDA Escalates a Recall, It Corrects Every Named Patient It Already Reassured, in Seconds.

Agent memory has a failure mode retrieval quality cannot fix: stale context. Similarity to a stored memory does not prove the memory is still true. For most agents that is embarrassing. In a pharmacy it is a Class I recall. Unsay's fix is a bitemporal schema on CockroachDB (`valid_from`/`valid_to` for the world, `asserted_at`/`retracted_at` for this system's belief), and a join no vector store can express: every answer still standing that leaned on a claim version we no longer believe. The replay still works past the AS OF SYSTEM TIME horizon, and every correction is exactly-once across a region failure.

CockroachDBHackathonCockroachDBBitemporal
Bloom: Nobody Cancels, They Just Stop Coming. Bloom Reads Each Client's Own Visit Rhythm, Flags the Ones Drifting from It, and Writes a Personal Note to Each. Rules Decide Who, Gemini Decides What to Say.
Jul.-Aug. 2026
12 min read

Bloom: Nobody Cancels, They Just Stop Coming. Bloom Reads Each Client's Own Visit Rhythm, Flags the Ones Drifting from It, and Writes a Personal Note to Each. Rules Decide Who, Gemini Decides What to Say.

A typical salon loses about 40% of its clients every year, a first-timer who does not rebook within 30 days has about a one-in-five chance of ever returning, and a loyal regular is worth several hundred dollars a year. The signal is invisible because it is an absence, spread across hundreds of people who each have their own rhythm. Bloom's architecture is the idea: rules decide WHO (deterministic risk engine on each client's own median visit gap), Gemini decides WHAT TO SAY (a short note in the owner's voice, referencing that person's real history). The demo of the whole thesis is two clients: Aisha and Jane are both 44 days since their last visit; Aisha comes every 8 weeks, Jane comes every 4. Any tool that flags 'no visit in 60 days' is wrong about one of them.

GeminiXPRIZEHackerFundGoogle Gemini 2.5 Flash
coldpath: The Ollama Windows-on-Arm Build Ships with the Matrix Unit Off. One-Line Fix Filed Upstream, 5.75x on Prompt Processing.
Aug. 2026
12 min read

coldpath: The Ollama Windows-on-Arm Build Ships with the Matrix Unit Off. One-Line Fix Filed Upstream, 5.75x on Prompt Processing.

I could not answer a basic question about my own Arm machine: when a local LLM runs, is it actually using the chip's matrix hardware, or silently falling back to scalar code? So I wrote the tool that answers it, pointed it at the ecosystem, and found the most popular Windows-on-Arm LLM runner cold. coldpath is a Capstone-based AArch64 disassembler that proves a binary contains SME, i8mm, and dotprod instructions; the finding is Ollama's official win-arm64 build has zero of any of them. One-line fix filed upstream (PR #17654), 5.75x recovered on prompt processing measured live on Azure Cobalt 100, gated in CI as a reusable GitHub Action.

ArmCloudAIAArch64Neoverse N2
Culprit: A Stack Trace for Model Decay. $90,322 of Model Error Traced to One Column That Went from Max 6 to Max 7.
Aug. 2026
13 min read

Culprit: A Stack Trace for Model Decay. $90,322 of Model Error Traced to One Column That Went from Max 6 to Max 7.

Freshness, volume, null-rate, and schema checks were all green. The model had been quietly wrong for six months. Culprit walked DataHub's ML lineage back to the column that did it, filed the incident into the graph with the dollars attached, wrote the fix, executed dbt build against the real warehouse to verify it, rejected its own first patch that would have deleted 87,693 rows, and only then opened the PR. $90,322 of attributable model error priced against a counterfactual control on 19.3M real NYC taxi records.

BuildWithDataHubHackathonDataHubMCP
Overtone: WCAG Audio Description for a Whole Video Archive at ~$0.03 a Minute, Generated in Place on Backblaze B2
Jul. 2026
11 min read

Overtone: WCAG Audio Description for a Whole Video Archive at ~$0.03 a Minute, Generated in Place on Backblaze B2

WCAG requires a spoken audio-description track on prerecorded video: a narrator describing what is on screen in the pauses in the dialogue. Public universities owe this under federal law by April 26, 2027, but human describers charge $15-$75 per finished minute, so archives go undescribed or get deleted. Overtone reads each video out of a Backblaze B2 bucket, generates the description in place, and writes the described master back beside the original. Measured on a real 3-minute MIT OpenCourseWare lecture: about nine cents to describe, roughly three cents a minute end to end. Every generative step runs through Genblaze, with automatic provider failover so an archive-scale run does not die on a single transient error.

BackblazeGenblazeHackathonBackblaze B2Genblaze
Viva: 91 Full-Mark C Submissions Out of 626 Hide a Defect the Autograder Never Checked
Jul. 2026
10 min read

Viva: 91 Full-Mark C Submissions Out of 626 Hide a Defect the Autograder Never Checked

A passing test suite proves a program worked on the inputs the instructor happened to check. It does not prove the student can explain what the program does. Viva closes that gap: GPT-5.6 reads the assignment and proposes an input the test suite never tried, Viva runs the student program and the reference on it, and it only asks the student a question when the two programs disagree. On 626 real C submissions that earned full marks, 91 (14.5%) hide a defect the autograder never checked, and 83 of those (13.3%) fail on an input as simple as `5 5 3`. Not a prediction, a replay: every finding carries a re-runnable command.

OpenAIBuildWeekGPT-5.6OpenAI Codex
LedgerPilot: Same Qwen, Same Ledger, Gate Off Posts 5 Wrong Entries and Gate On Posts 0
Jul. 2026
12 min read

LedgerPilot: Same Qwen, Same Ledger, Gate Off Posts 5 Wrong Entries and Gate On Posts 0

Month-end close is the wrong workflow to give a hallucinating AI a keyboard on. LedgerPilot is a month-end-close agent where Qwen proposes journal entries and a deterministic gate is the only path to a write. The counterfactual is the whole claim: same qwen-flash planner, same 39 close tasks, same live Odoo ledger, gate off posts 5 wrong entries (salaries paid out of Accounts Receivable, cost-of-goods to receivables and revenue, each one balanced, each one plausible), gate on posts 0. The model did not get better; the ledger did. Runs on Alibaba Cloud ECS, drives the write through MCP.

QwenCloudHackathonQwenAlibaba Cloud
Clatterfall: One Marble, One Subreddit, One Shared Run a Day, One Machine Nobody Can Build Alone
Jul. 2026
11 min read

Clatterfall: One Marble, One Subreddit, One Shared Run a Day, One Machine Nobody Can Build Alone

r/Clatterfall is one continuous descent, built by a crowd, one part per person per day. Every morning the entire committed machine re-runs as a single canonical simulation that everyone watches together, and the parts the marble abandons dissolve. Three rules do the load-bearing work: you can only build on the marble's actual path (the frontier), the daily run is server-simulated once and replayed pixel-identical everywhere, and parts the marble stops touching dissolve. Non-AI and proud: every pixel drawn from Phaser Graphics primitives at runtime.

RedditGamesHackathonReddit DevvitPhaser
Loose Ends: The Slack Agent That Refuses to Close a Loop Until It Sees Evidence
Jul. 2026
12 min read

Loose Ends: The Slack Agent That Refuses to Close a Loop Until It Sees Evidence

In a nonprofit or mutual-aid Slack, a dropped 'can someone follow up with the Diaz family?' is not a slipped deck, it is a person not served. Every commitment bot on the market marks a loop done when a timer fires or when someone clicks 'done'. Loose Ends watches the same channel for a later message that proves the work actually happened, and treats a deadline passing without proof as BROKEN. Grounded in a real number: 93% of social-services cases were marked closed, only 38% actually delivered the service (JAMA Network Open, 2024). Closed is not done.

SlackAgentHackSlack BoltSlack Real-Time Search
Pick Your Side: I Built a Machine That Manufactures a World Cup Team, Grounded in Real History
Jul. 2026
Winner
8 min read

Pick Your Side: I Built a Machine That Manufactures a World Cup Team, Grounded in Real History

Every World Cup app is built for the people who already care. This one is for everybody else: name any two nations and it reads their real history, picks the side you were always meant to love, and a stadium announcer swears you in. The whole architecture bends around one rule: none of the history is invented. Gemini researches with Google Search grounding on, then rewrites into a strict responseSchema, then ElevenLabs performs it over a stadium crowd that ElevenLabs also generated.

weekendchallengedevchallengegoogleai
FlakeWarden: 90.7% Accuracy and a 0% Safety False-Positive Rate on Flaky-Test Triage, on UiPath Maestro
Jun. 2026
Winner
11 min read

FlakeWarden: 90.7% Accuracy and a 0% Safety False-Positive Rate on Flaky-Test Triage, on UiPath Maestro

Flaky tests are the most corrosive failure mode in CI: a red build might be a real regression or just noise, and engineers eventually start ignoring red builds until a real bug ships. FlakeWarden answers the only question that matters (real defect, flaky, or environment) with a deterministic flake-scorer for the clear cases and a grounded UiPath Agent Builder classifier for the ambiguous ones, orchestrated through Maestro with a human approving every change. 90.7% accuracy on a 150-case corpus, with 0% safety false-positive rate enforced by mechanism.

AgentHackUiPathUiPath Maestro
LotZero: Zero Oversells and Zero Double-Spends on a Global Live Auction, Proven on Aurora DSQL
Jun. 2026
10 min read

LotZero: Zero Oversells and Zero Double-Spends on a Global Live Auction, Proven on Aurora DSQL

Real-time global commerce used to force a choice: a single-Region SQL box (correct but slow for distant bidders) or a multi-Region eventually-consistent store (fast but unsafe for money). Aurora DSQL collapses that tradeoff. LotZero puts the money ledger on DSQL and the social firehose on DynamoDB, then proves the invariant with a contention console that fires hundreds of concurrent global claims and measures: zero oversells, zero double-spends.

H0HackathonAmazon Aurora DSQLAmazon DynamoDB
OrbitOnboard: I Used All Four GitLab Orbit Query Types to Generate a Contributor Starter Kit in 10 Seconds
Jun. 2026
11 min read

OrbitOnboard: I Used All Four GitLab Orbit Query Types to Generate a Contributor Starter Kit in 10 Seconds

Half of new contributors abandon their first attempt to contribute to an unfamiliar codebase. Not because the problem is too hard, but because the map doesn't exist. OrbitOnboard generates that map by exercising all four Orbit query types in one coordinated workflow: critical files, reading order, expert map, similar past MRs, related open issues, posted directly as an issue comment.

GitLab OrbitKnowledge GraphDeveloper Experience