Should I Go Out? A Local Gemma Agent That Picks One Outdoor Plan, Then Tells You to Put Your Phone Away
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Title: Should I go out? One tap, one plan from a local Gemma, then put your phone away
Tags: devchallenge, hf26challenge, gemma, opensource
Cover image: ~/touch-grass-agent/docs/cover.png (upload it with "Add a cover image")
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This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass
What I Built
<!-- What does it do, and how does it get people off the screen and into the world? Who is it for? -->
Should I go out? is a web app that answers one question and then tells you to put your phone away.
You tap once. The app checks the weather, the air quality, and the parks around you, and Gemma, running on your own computer, suggests one outdoor plan that fits the time you have: a park or landmark, a round-trip route, a few things to do there, and what to wear. Then the screen tells you to put your phone in your pocket.
Apps that send you outside usually still want your attention, with feeds, streaks, and leaderboards. I wanted the screen to be the shortest part of the trip. You get one plan, and "Another place" if you don't like it. Goals and badges only move when you check in at the park with "I'm here", and the check-in compares your location in the browser without sending it anywhere.
On the first visit, four quick questions ask how you like to move, what you enjoy, who comes along, and whether you ride public bikes, or you can let the AI decide. Each suggestion also comes with a music video of about 30 seconds that flies over the real streets to the park in 3D. Once the app has loaded on your phone, check-ins work with no signal. It speaks English and Korean, because I live in Seoul.
It's for anyone who opens their phone on a free Saturday to find something to do and is still scrolling at sunset, and for parents who want a playground 20 minutes away on foot.
Demo
<!-- Share a deployed link or a video demo. -->
The model runs on your own Mac, so there's no public deployment. Here's the whole flow, from the questionnaire to the check-in:
!Answering the questionnaire, getting a walk to a park in Seoul, trying another place, opening the route map, switching the outfit, and checking in with "I'm here"
And the walk preview it makes for every suggestion:
!The walk preview: a 3D map of central Seoul, a location puck walking the route past 3D buildings, photos of the park with things to do, and an ending card with when to leave and be back
I took it outside
I'm a total homebody and rarely go out. While building the app, I tested it on my phone. It suggested 까치 어린이공원, a small park nearby that I didn't even know was there.
!The suggestion for 까치 어린이공원 and the weather panel
I walked over, checked in, and got my first badge. Going somewhere because of an app I built myself felt refreshing.
!The playground with a tube slide, the park's opening hours sign, and the check-in card with the First steps badge
One thing was off. After I checked in, it suggested "a quick game of soccer at the sports field", but the park has no sports field. The app had counted a field just outside the park as part of it, so I had the agent fix that in the afternoon: only what's inside a park's outline counts now (83df1d5). Using it on my phone had also turned up two bugs earlier, a "Walk there" button that didn't respond and a start point shown as a plus code. Session 6 below covers both.
Code
<!-- Show us the code! You can embed a GitHub repo directly into your post. -->
{% github scs0209/touch-grass-agent %}
It's a pnpm monorepo with apps/server (Hono and Mastra on Node) and apps/web (Vite and React). The README has a short setup, and AGENTS.md lets a coding agent set everything up for you, including pulling the model.
How I Built It
<!-- Which open-source AI did you use (open-weight models, agent harnesses, frameworks, local inference), and how is your project built around it? -->
| Piece | What it does here |
|---|---|
| Gemma 3 4B (open weights) | Picks the place, writes the reason and the things to do, picks the outfit, and translates the answer into Korean |
| Ollama (local inference) | Runs Gemma on my Mac through its OpenAI-compatible endpoint |
| Mastra (open-source agent framework) | The agent, and a four-step workflow around it |
| OpenStreetMap data via Nominatim, Overpass, and OSRM | Parks and landmarks, what each park has (playground, water, viewpoint), and real walking and cycling routes |
| Open-Meteo | Weather and air quality |
| MapLibre GL JS and OpenFreeMap | The 3D flyover in the walk preview |
All of these are free and need no API key.
What the server does and what Gemma does
My first version gave Gemma the parks and asked it to plan. It made up walking times, and the card disagreed with the map. Now the server handles everything that can be measured, and Gemma handles the parts that need judgment. Each request is a four-step Mastra workflow:
1. Gather conditions from Open-Meteo, Nominatim, and Overpass, and measure the real round trip to every park with one OSRM request. Parks that don't fit your time are dropped.
2. Ask Gemma. It sees only the parks that fit, each with its roundTripMin and features, and answers in JSON: one park by id, a reason, things to do, and an outfit from a fixed catalog. It can't invent coordinates or clothes.
3. Check the answer. zod validates the shape. Rules drop things to do that name a facility the park doesn't have, and reasons that don't match the weather. If you said "with kids" and Gemma picked a park with no playground while another candidate has one, the server switches parks. If Gemma fails twice, simple rules answer instead.
4. Plan the route. Step 1 already measured it, so it comes from the cache.
Korean answers come from a second Gemma call that translates the checked English answer, with place names masked so they come back unchanged.
Tracing with Sentry
With SENTRY_DSN set, each request is one Sentry trace: the four steps, the agent run, every Gemma call with its tokens, and every outgoing API request.
!Sentry trace of one recommendation: the OSRM round-trip request runs during gather-conditions, which stops waiting for the slow Overpass request after 3 seconds
The public Overpass server is often slow, so the app waits at most 3 seconds for it and caches park features for a day. In this trace the route step takes 1 ms, because the route was measured during that wait.
Most of the wait is Gemma. Three requests on an M3 Pro MacBook, with the model already loaded:
| | Input tokens | Output tokens | Time |
|---|---|---|---|
| Gemma picks the place and writes the plan | 2,151 to 2,592 | 218 to 236 | 9.0 to 9.7 s |
| Gemma translates the answer into Korean | 523 | 73 | 3.2 s |
| The whole request | | | 12.6 to 15.2 s |
That's 75 to 80% of every request, and it costs $0. When Gemma returns invalid JSON, the retry shows up in the same step:
!Sentry trace where ask-gemma calls the agent twice: the first answer was invalid, the second one passed
Why Does Open Innovation Matter?
<!-- Why does open innovation matter for what you built? What did it make possible that a closed API wouldn't? -->
This app knows where you are, when you go out, who you go with, and which parks you visit. With a closed model API, all of that would sit in someone else's logs. Here the prompt never leaves my Mac. The map and weather services get only the coordinates they need, Kakao Map gets the start and the destination when you tap directions in Korea, and check-ins stay in the browser.
Because it's free to run, I kept calls I'd probably have cut with a paid API: "Another place" asks Gemma again, every answer gets a second call for Korean, and broken JSON gets a retry.
The small local model shaped the design too: measured routes, a fixed outfit catalog, rule checks, and a fallback. Swapping models is one environment variable (OLLAMA_MODEL), and the same checks apply to any open model. There's no sign-up and no API key. Clone it, pull the model, and it works anywhere OpenStreetMap, Open-Meteo, and OSRM cover. The catch is that the Mac has to be awake, and the first answer takes 10 to 20 seconds while Gemma loads.
My Agent Session
<!-- Optional, but judges love it. Save your session with DevRelay and embed it with the agent_session tag (see the challenge page), or link to it. -->
I built this with Cursor agents, and Entire recorded the sessions and linked each one to its commit: 104 of the 126 commits carry an Entire-Checkpoint: trailer. The records are in a private repository because they also include my work setup.
DEV's parser doesn't read Cursor transcripts yet, so I converted the sessions to the Claude Code format, which is why they show a Claude Code icon. Sessions 1 to 5 come from Entire's records and session 6 from the Cursor chat. Cursor doesn't keep tool results, so you'll see my prompts, the agent's replies, and the commands it ran. My prompts were in Korean, so I added English translations, and I redacted local paths and account names.
1. "The card and the route show different times. Fix that."
The card and the map disagreed on the walk because Gemma was estimating the minutes. The agent moved route measurement in front of Gemma: OSRM measures every candidate's round trip, parks that don't fit are dropped, and Gemma gets each park's roundTripMin. After this I stopped asking a 4B model for numbers I could measure.
Commit: b94354b "keep only parks whose round trip fits"
{% agent_session 611 %}
2. "Find where caching actually helps and apply it", then "I need numbers from before and after"
The agent added one shared in-memory cache (apps/server/src/cache.ts) with a lifetime for each kind of data: 10 minutes for the same suggestion and for weather within about 1 km, a day for place searches and routes, 6 hours for park photos, and never for live bike counts. Identical requests share one Gemma call, and an older weather reading stands in when Open-Meteo fails. It also keeps the app from hammering Nominatim and Overpass, which are free public services.
When I asked for numbers, it wrote a benchmark that runs the commits from before and after caching side by side in git worktrees, alternating the runs so changes in network speed hit both.
| Scenario | Before | After |
|---|---|---|
| Reopen the same preview | 5.5 s | 4 ms |
| Search the same city again | 10.3 s | 0.1 s |
| 3 identical requests at once | 22.8 s | 12.1 s |
| A whole six-step session | 58.8 s | 39.2 s |
| Outside API calls per session | 28 | 17 |
| Gemma calls per session | 8 | 5 |
It also reported what didn't get faster. A first visit has nothing to reuse, and caching the data around Gemma saves only 1 to 2 seconds. The big gains all come from requests Gemma doesn't have to answer again.
Commits: 5512232, 550c51f, 2bdb30c. Method and raw runs: docs/benchmarks/caching.md
{% agent_session 610 %}
3. "When you find gaps, fix them yourself. Make it a rule or a loop."
The agent wrote a rule into AGENTS.md: after every change, verify it, read the results critically, and fix what's wrong, for up to 3 rounds. It asks first only about product decisions, new services, or destructive steps. Its first catch was Gemma picking a park that matched none of my questionnaire answers while another candidate did. The server now switches to the matching park.
Commits: 07a2a72 "close gaps before reporting", 2bd7121 "switch to a park that matches the quiz answers"
{% agent_session 609 %}
4. "Yes, fix it." (re-capturing the Sentry trace for the README)
While re-capturing the trace, the agent spotted the Seoul bike API key in the span names. That API takes its key in the URL path, and Sentry records outgoing request URLs. It didn't use that screenshot. It masked the key in beforeSendSpan and beforeBreadcrumb, and captured a clean trace.
Commit: 1601876 "mask the Seoul API key in Sentry spans"
{% agent_session 613 %}
5. "Isn't biking a matter of taste? Base it on what people picked on their first visit."
People who skipped the bike question could still get a bike suggestion. Now the app looks up bike stations only when someone answers yes to "Public bikes?".
Commit: 8568245 "suggest bikes only when the user opts in"
{% agent_session 608 %}
6. "I tapped 'Walk there' on my phone and nothing happened", then "Why is the start a plus code?"
For the dead button, the agent emulated an iPhone in headless Chrome and checked which element was under it. It was the guided tour's full-screen layer. The tour now leaves a hole at the spotlight, and tapping the highlighted button ends the tour.
For the plus code, my first reaction was "just write the names properly." The agent came back with tests. With names only, Google Maps opened 대치목련공원 when asked for 삼성목련공원, and sent "College Green" in London to a college in Paddington. Google Maps also has no walking directions in Seoul. So I picked Kakao Map for Korea, since its link takes a name and exact coordinates for every stop. The start is now a short street address from Nominatim, like "마포구 월드컵북로2길 11". The agent skipped the nearest shop name on purpose: "홍대 순대국" as a start point reads as if you were inside the restaurant.
Commits: 960e21e, 40032c2, f261dcc
{% agent_session 607 %}
Credits
The code is MIT licensed. Icons are Microsoft's Fluent Emoji (MIT), the glass styling comes from fengshao1227/ccg-workflow (MIT), and the walk preview's editing follows ideas from OpenMontage without copying code. Map data is © OpenStreetMap contributors, weather is from Open-Meteo (CC BY 4.0), and park photos are from Mapillary contributors (CC BY-SA 4.0) and Wikimedia Commons. The full list is in the README.
Prize Categories
<!-- Which partner categories are you entering? List every one that applies, or remove this section. -->
- Best Use of Gemma: Gemma 3 4B runs locally through Ollama. It picks the place, writes the plan and the outfit, and translates every answer into Korean with a second call.
- Best Use of Mastra: a Mastra agent over a local open model, inside a four-step Mastra workflow (gather, ask, check, route) with retries and a rule-based fallback.
- Best Use of Sentry Agent Tracing: every request is one trace with the workflow steps, agent runs, Gemma calls with tokens, and outgoing API requests. The traces show that Gemma is 75 to 80% of each request, make its retries visible, and exposed an API key in span names before a screenshot went out.
- Best Use of Entire: Entire recorded the Cursor sessions and linked them to 104 of the 126 commits, and the embeds for sessions 1 to 5 above were rebuilt from those records.
<!-- Thanks for participating! -->
5 reactions · 5 comments
Mentions
Should I Go Out? A Local Gemma Agent That Picks One Outdoor Plan, Then Tells You to Put Your Phone Away <!-- Title: Should I go out? One tap, one plan from a local Gemma, then put your phone away Tags: devchallenge, hf26challenge, gemma, opensource Cover image: ~/touch-grass-agent/docs/cover.png (upload it with "Add a cover image") --> This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass What I Built <!-- What does it do, and how does it get people off the screen and into the world? Who is it for? --> Should I go out? is a web app that answers one question and then tells you to put your phone away. You tap once. The app checks the weather, the air quality, and the parks around you, and Gemma, running on your own computer, suggests one outdoor plan that fits the time you have: a park or landmark, a round-trip route, a few things to do there, and what to wear. Then the screen tells you to put your phone in your pocket. Apps that send you outside usually still want your attention, with feeds, streaks, and leaderboards. I wanted the screen to be the shortest part of the trip. You get one plan, and "Another place" if you don't like it. Goals and badges only move when you check in at the park with "I'm here", and the check-in compares your location in the browser without sending it anywhere. On the first visit, four quick questions ask how you like to move, what you enjoy, who comes along, and whether you ride public bikes, or you can let the AI decide. Each suggestion also comes with a music video of about 30 seconds that flies over the real streets to the park in 3D. Once the app has loaded on your phone, check-ins work with no signal. It speaks English and Korean, because I live in Seoul. It's for anyone who opens their phone on a free Saturday to find something to do and is still scrolling at sunset, and for parents who want a playground 20 minutes away on foot. Demo <!-- Share a deployed link or a video demo. --> The model runs on your own Mac, so there's no public deployment. Here's the whole flow, from the questionnaire to the check-in: !Answering the questionnaire, getting a walk to a park in Seoul, trying another place, opening the route map, switching the outfit, and checking in with "I'm here" And the walk preview it makes for every suggestion: !The walk preview: a 3D map of central Seoul, a location puck walking the route past 3D buildings, photos of the park with things to do, and an ending card with when to leave and be back I took it outside I'm a total homebody and rarely go out. While building the app, I tested it on my phone. It suggested 까치 어린이공원, a small park nearby that I didn't even know was there. !The suggestion for 까치 어린이공원 and the weather panel I walked over, checked in, and got my first badge. Going somewhere because of an app I built myself felt refreshing. !The playground with a tube slide, the park's opening hours sign, and the check-in card with the First steps badge One thing was off. After I checked in, it suggested "a quick game of soccer at the sports field", but the park has no sports field. The app had counted a field just outside the park as part of it, so I had the agent fix that in the afternoon: only what's inside a park's outline counts now (83df1d5). Using it on my phone had also turned up two bugs earlier, a "Walk there" button that didn't respond and a start point shown as a plus code. Session 6 below covers both. Code <!-- Show us the code! You can embed a GitHub repo directly into your post. --> {% github scs0209/touch-grass-agent %} It's a pnpm monorepo with apps/server (Hono and Mastra on Node) and apps/web (Vite and React). The README has a short setup, and AGENTS.md lets a coding agent set everything up for you, including pulling the model. How I Built It <!-- Which open-source AI did you use (open-weight models, agent harnesses, frameworks, local inference), and how is your project built around it? --> | Piece | What it does here | |---|---| | Gemma 3 4B (open weights) | Picks the place, writes the reason and the things to do, picks the outfit, and translates the answer into Korean | | Ollama (local inference) | Runs Gemma on my Mac through its OpenAI-compatible endpoint | | Mastra (open-source agent framework) | The agent, and a four-step workflow around it | | OpenStreetMap data via Nominatim, Overpass, and OSRM | Parks and landmarks, what each park has (playground, water, viewpoint), and real walking and cycling routes | | Open-Meteo | Weather and air quality | | MapLibre GL JS and OpenFreeMap | The 3D flyover in the walk preview | All of these are free and need no API key. What the server does and what Gemma does My first version gave Gemma the parks and asked it to plan. It made up walking times, and the card disagreed with the map. Now the server handles everything that can be measured, and Gemma handles the parts that need judgment. Each request is a four-step Mastra workflow: 1. Gather conditions from Open-Meteo, Nominatim, and Overpass, and measure the real round trip to every park with one OSRM request. Parks that don't fit your time are dropped. 2. Ask Gemma. It sees only the parks that fit, each with its roundTripMin and features, and answers in JSON: one park by id, a reason, things to do, and an outfit from a fixed catalog. It can't invent coordinates or clothes. 3. Check the answer. zod validates the shape. Rules drop things to do that name a facility the park doesn't have, and reasons that don't match the weather. If you said "with kids" and Gemma picked a park with no playground while another candidate has one, the server switches parks. If Gemma fails twice, simple rules answer instead. 4. Plan the route. Step 1 already measured it, so it comes from the cache. Korean answers come from a second Gemma call that translates the checked English answer, with place names masked so they come back unchanged. Tracing with Sentry With SENTRY_DSN set, each request is one Sentry trace: the four steps, the agent run, every Gemma call with its tokens, and every outgoing API request. !Sentry trace of one recommendation: the OSRM round-trip request runs during gather-conditions, which stops waiting for the slow Overpass request after 3 seconds The public Overpass server is often slow, so the app waits at most 3 seconds for it and caches park features for a day. In this trace the route step takes 1 ms, because the route was measured during that wait. Most of the wait is Gemma. Three requests on an M3 Pro MacBook, with the model already loaded: | | Input tokens | Output tokens | Time | |---|---|---|---| | Gemma picks the place and writes the plan | 2,151 to 2,592 | 218 to 236 | 9.0 to 9.7 s | | Gemma translates the answer into Korean | 523 | 73 | 3.2 s | | The whole request | | | 12.6 to 15.2 s | That's 75 to 80% of every request, and it costs $0. When Gemma returns invalid JSON, the retry shows up in the same step: !Sentry trace where ask-gemma calls the agent twice: the first answer was invalid, the second one passed Why Does Open Innovation Matter? <!-- Why does open innovation matter for what you built? What did it make possible that a closed API wouldn't? --> This app knows where you are, when you go out, who you go with, and which parks you visit. With a closed model API, all of that would sit in someone else's logs. Here the prompt never leaves my Mac. The map and weather services get only the coordinates they need, Kakao Map gets the start and the destination when you tap directions in Korea, and check-ins stay in the browser. Because it's free to run, I kept calls I'd probably have cut with a paid API: "Another place" asks Gemma again, every answer gets a second call for Korean, and broken JSON gets a retry. The small local model shaped the design too: measured routes, a fixed outfit catalog, rule checks, and a fallback. Swapping models is one environment variable (OLLAMA_MODEL), and the same checks apply to any open model. There's no sign-up and no API key. Clone it, pull the model, and it works anywhere OpenStreetMap, Open-Meteo, and OSRM cover. The catch is that the Mac has to be awake, and the first answer takes 10 to 20 seconds while Gemma loads. My Agent Session <!-- Optional, but judges love it. Save your session with DevRelay and embed it with the agent_session tag (see the challenge page), or link to it. --> I built this with Cursor agents, and Entire recorded the sessions and linked each one to its commit: 104 of the 126 commits carry an Entire-Checkpoint: trailer. The records are in a private repository because they also include my work setup. DEV's parser doesn't read Cursor transcripts yet, so I converted the sessions to the Claude Code format, which is why they show a Claude Code icon. Sessions 1 to 5 come from Entire's records and session 6 from the Cursor chat. Cursor doesn't keep tool results, so you'll see my prompts, the agent's replies, and the commands it ran. My prompts were in Korean, so I added English translations, and I redacted local paths and account names. 1. "The card and the route show different times. Fix that." The card and the map disagreed on the walk because Gemma was estimating the minutes. The agent moved route measurement in front of Gemma: OSRM measures every candidate's round trip, parks that don't fit are dropped, and Gemma gets each park's roundTripMin. After this I stopped asking a 4B model for numbers I could measure. Commit: b94354b "keep only parks whose round trip fits" {% agent_session 611 %} 2. "Find where caching actually helps and apply it", then "I need numbers from before and after" The agent added one shared in-memory cache (apps/server/src/cache.ts) with a lifetime for each kind of data: 10 minutes for the same suggestion and for weather within about 1 km, a day for place searches and routes, 6 hours for park photos, and never for live bike counts. Identical requests share one Gemma call, and an older weather reading stands in when Open-Meteo fails. It also keeps the app from hammering Nominatim and Overpass, which are free public services. When I asked for numbers, it wrote a benchmark that runs the commits from before and after caching side by side in git worktrees, alternating the runs so changes in network speed hit both. | Scenario | Before | After | |---|---|---| | Reopen the same preview | 5.5 s | 4 ms | | Search the same city again | 10.3 s | 0.1 s | | 3 identical requests at once | 22.8 s | 12.1 s | | A whole six-step session | 58.8 s | 39.2 s | | Outside API calls per session | 28 | 17 | | Gemma calls per session | 8 | 5 | It also reported what didn't get faster. A first visit has nothing to reuse, and caching the data around Gemma saves only 1 to 2 seconds. The big gains all come from requests Gemma doesn't have to answer again. Commits: 5512232, 550c51f, 2bdb30c. Method and raw runs: docs/benchmarks/caching.md {% agent_session 610 %} 3. "When you find gaps, fix them yourself. Make it a rule or a loop." The agent wrote a rule into AGENTS.md: after every change, verify it, read the results critically, and fix what's wrong, for up to 3 rounds. It asks first only about product decisions, new services, or destructive steps. Its first catch was Gemma picking a park that matched none of my questionnaire answers while another candidate did. The server now switches to the matching park. Commits: 07a2a72 "close gaps before reporting", 2bd7121 "switch to a park that matches the quiz answers" {% agent_session 609 %} 4. "Yes, fix it." (re-capturing the Sentry trace for the README) While re-capturing the trace, the agent spotted the Seoul bike API key in the span names. That API takes its key in the URL path, and Sentry records outgoing request URLs. It didn't use that screenshot. It masked the key in beforeSendSpan and beforeBreadcrumb, and captured a clean trace. Commit: 1601876 "mask the Seoul API key in Sentry spans" {% agent_session 613 %} 5. "Isn't biking a matter of taste? Base it on what people picked on their first visit." People who skipped the bike question could still get a bike suggestion. Now the app looks up bike stations only when someone answers yes to "Public bikes?". Commit: 8568245 "suggest bikes only when the user opts in" {% agent_session 608 %} 6. "I tapped 'Walk there' on my phone and nothing happened", then "Why is the start a plus code?" For the dead button, the agent emulated an iPhone in headless Chrome and checked which element was under it. It was the guided tour's full-screen layer. The tour now leaves a hole at the spotlight, and tapping the highlighted button ends the tour. For the plus code, my first reaction was "just write the names properly." The agent came back with tests. With names only, Google Maps opened 대치목련공원 when asked for 삼성목련공원, and sent "College Green" in London to a college in Paddington. Google Maps also has no walking directions in Seoul. So I picked Kakao Map for Korea, since its link takes a name and exact coordinates for every stop. The start is now a short street address from Nominatim, like "마포구 월드컵북로2길 11". The agent skipped the nearest shop name on purpose: "홍대 순대국" as a start point reads as if you were inside the restaurant. Commits: 960e21e, 40032c2, f261dcc {% agent_session 607 %} Credits The code is MIT licensed. Icons are Microsoft's Fluent Emoji (MIT), the glass styling comes from fengshao1227/ccg-workflow (MIT), and the walk preview's editing follows ideas from OpenMontage without copying code. Map data is © OpenStreetMap contributors, weather is from Open-Meteo (CC BY 4.0), and park photos are from Mapillary contributors (CC BY-SA 4.0) and Wikimedia Commons. The full list is in the README. Prize Categories <!-- Which partner categories are you entering? List every one that applies, or remove this section. --> - Best Use of Gemma: Gemma 3 4B runs locally through Ollama. It picks the place, writes the plan and the outfit, and translates every answer into Korean with a second call. - Best Use of Mastra: a Mastra agent over a local open model, inside a four-step Mastra workflow (gather, ask, check, route) with retries and a rule-based fallback. - Best Use of Sentry Agent Tracing: every request is one trace with the workflow steps, agent runs, Gemma calls with tokens, and outgoing API requests. The traces show that Gemma is 75 to 80% of each request, make its retries visible, and exposed an API key in span names before a screenshot went out. - Best Use of Entire: Entire recorded the Cursor sessions and linked them to 104 of the 126 commits, and the embeds for sessions 1 to 5 above were rebuilt from those records. <!-- Thanks for participating! -->