Santiago Gonzalez Toral

Posted on Oct 12

Idea Leash: an open-source walk buddy with a local model for a brain

#devchallenge #hf26challenge

Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass Submission 🌿

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass

What I Built

Idea Leash is how I take a break from work and touch grass. I go out with my dog, or for an evening walk with a podcast in my ears, and I wanted those walks to leave something behind: what I heard, the ideas that show up while I'm moving, and a small reward for going out. It is for anyone who walks with a podcast and wants to keep some of it.

The AI runs on open models on my Mac: Gemma 4 12B through Ollama, the Hermes agent, and whisper.cpp for speech. On the phone I use two apps. The Leash app records the route and my voice, and Telegram is where I talk to the Leash agent.

The Leash agent is an ambient agent: it speaks first. When a podcast is ready, when a walk has been saved and when I answer a quiz question, the Mac messages me in Telegram without being asked, and I reply there. It can also bring back one concept and one older note from my wiki when I ask. I have not built scheduled nudges, so it speaks when something happened, not on a timer.

The loop:

Before the walk I send an episode link to a Telegram bot. The Mac transcribes it, writes notes into a markdown wiki (my second brain) and prepares a five question quiz. Every question quotes the transcript, and a script checks the quote.
I walk with the phone in my pocket and listen in my usual podcast player. The web app records the route, and one big button records a voice idea.
When I stop, the Mac sends one Telegram message: distance, time, my ideas, and the top links for any note that started with "search the web for...".
I say "quiz me". Four multiple choice questions arrive as Telegram buttons. A podcast competes with the street for my attention, so the fifth asks me to connect an idea from the episode to something I saw on the walk.
Sixteen badges reward kilometers, time with the dog, streaks, voice ideas and quiz results. Code computes them, not the model.

The theme asks for the screen to be the shortest part. On the walk I touch the phone only to start, to record a note and to stop. The screen does stay on behind a black cover, because in Chrome a hidden web page gets no GPS.

Demo

A walkthrough of how I use the Leash app and its ambient agent to rest from work and touch grass, going out with my dog or out to exercise. Along the way it keeps a second brain of what I learned and the ideas I had, with the AI running on my Mac.

I pick the kind of walk (podcast, dog or plain), pocket the phone behind the black cover, and hold a corner for three seconds to stop.

Afterwards the Activity view shows my totals, the streak and recent walks.

The Badges view shows what I have earned and how far I am from the next badge.

This is the Telegram message I've got from the agent after Sunday morning's offline dog walk:

Dog walk saved: 921 m in 24 min.
The phone could not track 2 min of it (screen off or another app in front).
2 voice ideas saved:
  - Remind me to check out the milestones of the project as we are back f…
  - Search on the web about libraries for 2D gaming and physics engine.
Searched "libraries for 2D gaming and physics engine":
  1. Most Popular C[++] Open-Source Physics Engines
     https://www.reddit.com/r/gamedev/comments/qu1y2y/most_popular_c_opensource_physics_engines/
  2. physics-engine · GitHub Topics
     https://github.com/topics/physics-engine
  3. Free 2D game engine library for teaching purposes
     https://www.facebook.com/groups/132728896890594/posts/2143324715830992/

Code

santteegt / HacktoberFest2026

Hacktoberfest 2026

Monorepo for my Hacktoberfest 2026 challenge entries and the knowledge base behind them.

Hacktoberfest 2026 is run by MLH and DEV, presented by DigitalOcean. The theme is "AI belongs to everyone": build with open-weight models and open-source AI. Pull requests no longer count. Rewards come from virtual stickers (see STICKERS.md), and the DEV Challenges carry cash prizes.

Challenges

Each challenge lives in its own folder under challenges/. I'm starting with the weekend challenge and adding the others as they open.

#	Challenge	Window (UTC)	Status
01	Weekend: Build for a Friend: RC Pit Companion	Oct 2 02:00 → Oct 5 06:59	✅ Published
02	Week 1: Touch Grass: Idea Leash	Oct 5 18:00 → Oct 12 06:59	🟡 Built and tested on a real phone; demo and post pending (not published)
03	Week 2: Open-Source AI	Oct 12 → Oct 19	🔵

…

View on GitHub

The code, the setup steps and the dated log of everything can be found in the challenges/02-week1-touch-grass folder. MIT licensed.

How I Built It

The numbers in the diagram follow one loop:

A link I send in Telegram reaches the Leash agent, a Hermes profile in a macOS VM. The agent runs leashctl, which calls the tool server on the Mac through an SSH reverse tunnel. The server fetches the audio from the podcast host, graphify transcribes it, Gemma writes the notes and the quiz, the notes go into the wiki, and Piper makes spoken versions of the questions (not used in the Telegram quiz yet).
The same tool server serves the phone app over Tailscale HTTPS, reachable only from my tailnet. A finished walk uploads its GPS track, and the Mac recomputes distance and time.
Voice clips travel with the walk. whisper.cpp transcribes each voice note (a 15 second clip in 0.95 s) and it is saved as an idea in the wiki. A note that starts with a search phrase becomes one SerpApi search.
Code builds the after-walk message, and the Mac sends it to Telegram.
"Quiz me" goes to the agent, which asks each multiple choice question as buttons.
Each answer goes back through leashctl. The Mac grades it and sends the grade itself.

The stack:

Job	What runs it	License	Where
Language model (notes, quiz, grading wording, chat)	Gemma 4 12B, 4 bit QAT build, through Ollama	Apache 2.0	my Mac
Agent in Telegram	Hermes Agent v0.21.6, one profile	MIT	a macOS VM on the Mac, run by Lume (MIT)
Speech to text for voice notes	whisper.cpp with Whisper large-v3-turbo	MIT	my Mac
Podcast transcripts	graphify with faster-whisper	Apache 2.0 / MIT	my Mac
Spoken versions of the quiz questions (made at ingest, not used in the Telegram quiz yet)	Piper, run as a separate process	GPL-3.0-or-later	my Mac
Tool server: walks, badges, quiz checks, messages	Python, standard library only	MIT (this project)	my Mac
Phone app	one HTML file, no framework	MIT (this project)	Chrome on Android
Phone to Mac link	Tailscale HTTPS, tailnet only	not open	phone and Mac
The second brain	an OKF markdown wiki	plain files I own	my Mac

Two agent skills

The agent's behaviour lives in two skills in the repo's skills/ folder. A skill is a SKILL.md of instructions plus scripts the agent can run.

podcast-quiz turns a transcript into four multiple-choice questions and one field-link question, and grades spoken answers afterwards. Its scripts check every question's quote against the transcript, the spoken length and the answer-length bias, and shuffle the answer positions.
leash-tools tells the agent how to reach the Mac with one command, leashctl: ingest, next, answer, idea, search, resurface, walks and audio.

I used the skill-creator skill workflow for implementing them: write the skill, run it on three test transcripts with the local Gemma model, read the failures, change the skill, run again. It took four rounds. The checking script did more than the prose: with it, the skill passed 27 of 27 checks, against 20 of 27 for a plain prompt.

Telegram taught me a second lesson. The 12B model did not follow a paragraph in a skill and wrote a question as plain text instead of calling the buttons tool. So the instruction moved into the tool's reply, in an action field the agent only has to follow. agent/setup-profile.sh installs the Hermes profile with both skills.

The rule I ended up with: the model does language jobs, and code does everything else. Distances, scores, badges, streaks, scheduling, and every message that has to be true.

Where it ended up

Every part of the loop has run on a real phone and a real walk: the link and the notes, the walk, the offline upload, the after-walk message, the quiz in Telegram and voice search. In one quiz retake after the grades moved to the Mac, the model stayed silent after each multiple-choice answer. By Sunday, the app had counted four walks: 7.66 km, 112 minutes, nine voice ideas, a three-day streak and 7 of 16 badges. That is one person and one phone.

Limits:

The 12B model is not a reliable tool user. In my tests, it sometimes skipped a command and described it as done.
Grading of the open question is unmeasured. I have three data points.
English only, and speech recognition mishears names.
The Mac has to be awake. While it sleeps, the bot is offline, and a finished walk waits on the phone. Used caffeinate to keep my laptop awake while going out.
The screen stays on during a walk, behind the black cover.

Why Does Open Innovation Matter?

Idea Leash asks for a lot of trust. It hears my voice, keeps the routes I walk with my dog, and builds a private memory of what I learn. Open models and an open agent harness made it buildable on those terms, because every part that thinks runs on my own Mac, where I can look inside it and change it.

No hosted model hears my voice or sees my route. Gemma 4 12B answers through Ollama, whisper.cpp transcribes voice notes on the Mac (a 15-second clip in 0.95 s), and the GPS track is stored there. Ollama's log from the agent's first day, tests included, shows 183 chat requests, all to the local model. My reasoning, not a test: with a hosted model, every voice idea and episode transcript would pass through a server I do not control.

Free calls shaped the design. The quiz generator checks every quote against the transcript and regenerates up to three times when a check fails. With a bill per call, I would have weighed each retry and each test run against the cost, and I suspect I would have tested less.

I could open it up. Ollama shows which model is loaded and with what context, which is how I caught it reloading at every hand-off. Hermes keeps its chat history in SQLite, so I read what the model actually did instead of trusting its summary. The agent is a folder of files (persona, allowed tools, two skills), versioned and tested like code, so I decided which jobs the model gets. Grades now come from code. The model is one alias in one file, and the speech model, the voice and the harness can each be swapped.

The second brain is plain OKF markdown, so any tool or agent can read it, and nothing I learn is locked in a product. One script installs the agent profile with both skills on a Hermes instance. I have run it in one VM only, so "someone else could run their own Leash on their own Mac" is a design goal, not a result. It is also how one person put this together inside the challenge week, from open parts that each do one job.

Not all of it is open. Telegram carries my messages and voice notes, Tailscale links the phone to the Mac, search words go to SerpApi, the audio comes from the podcast host, and the agent's VM runs macOS.

My Agent Session

I built this with Claude Code, with subagents for brainstorming and for the app's icon, badge and layout design. The session below is an abridged transcript of 106 messages: my prompts and the final reply of each turn, shortened, with tool calls and private details removed. The first Telegram tests are the most useful part: a token the shell tool never got, and an old error the model replayed from its chat history.

Building Idea Leash: a walk-and-podcast quiz agent with Gemma, Hermes and a phone app

You

let's scaffold the folder for Week1 challenge

Agent

The Week 1 (Touch Grass) folder is scaffolded at challenges/02-week1-touch-grass. I haven't tried to build or run anything, and I haven't committed.

Challenge facts (live data, event 79)
- The theme is Touch Grass. The project must put open-source AI at the core and get people off the screen and into the world. Bonus points if you take it outside and report how it went.
- The deadline is Oct 12 06:59 UTC, which is Sun Oct 11, 11:59 PM PDT.
- The tag is #hf26challenge.
- The prizes and the 16 partner categories are unchanged from the weekend. Judging weights the write-up most heavily.
- Start-time mismatch: the DEV API now says Oct 5 18:00 UTC. The schedule feed said 07:00 and an earlier API read said 16:00.

[trimmed for the transcript]

You

check if anyone on DEV have reported how to use a gemma model on mobile for custom agentic apps

Agent

Yes. DEV has a lot on this, mostly from the Gemma 4 Challenge in May and since. These are the posts worth reading, grouped by how you'd build.

Native Android with Google's LiteRT-LM (the most direct route)

On-Device AI with the Google AI Edge Gallery and Gemma 4 (Google AI, Apr 2026) is the official starting point.
- The open-source Gallery app (Kotlin and Swift) runs Gemma 4 E2B/E4B on both Android and iOS.
- It has an Agent Skills system (SKILL.md files with JavaScript or native intents), a Function Calling Guide using @Tool/@ToolParam, and a fine-tuned FunctionGemma 270M for device control.

[trimmed for the transcript]

You

I'm going to give you a quick overview of my outdoor everyday activities. Spin up a brainstorming agent using Opus 5.5 to propose 3-5 ideas for the this week challenge based on that context and the challenge theme. Ideas should be fun, might include my dog, might be helpful to reduce the stress while keeping my attention levels high so I can keep motivated to continue working on projects, maybe help me with critical thinking and foster creativity, help me exercise to trade-off the hours I spent in front of my laptop:

I commute from home to the my aunt's house ~20-min by car in the morning. Nearby there's a park
Take a 20-25 min with my dog around the park
Come back to my aunt's and do home office until 1pm. She has a small garden where I play with my dog from time to time to relax between work sessions
In the afternoon, I work at home. At night, around 7:30pm, I drive to the same park again and do some stretching exercises, and then a 30-min fast walk

Agent

The Opus 5.5 brainstorming agent finished. It read the rules, the playbook and the wiki, and it wrote no files. The ideas below are its output; I've shortened them and added a few of my own notes.

One design choice runs through all of them. The phone only records and plays audio, and the AI runs on your Mac before you leave or after you're back. That avoids the phone-inference problem from the weekend. The catch is that nothing runs in the park, so the post has to say so plainly. The "why open" argument becomes that your voice, work and location stay on your Mac and it costs nothing to run daily.

[trimmed for the transcript]

You

let's do another ideas brainstorming iteration:

it should include some king of outside activity
if the model runs on the laptop pre/post going outside, the phone could act as a multi-modal sensor device. This data could be used together with some public APIs if possible, to include gamification into the AI side of the idea

Agent

The brainstorming agent is working on the second round with your new constraints. They are: an outdoor activity at the core, the phone as a sensor, public APIs, and gamification driven by the AI. I'll pass along the ideas when its report comes back.

You

is the agent using Opus 5.5 ?

Agent

Round 2 is back from the brainstorming agent. Nothing was written to disk. The agent worked from memory on API terms, so each uncertain claim is marked (check) and still needs a day-one test. I've condensed the report.

Setup common to all four ideas
- The phone is only a sensor outside: photos, voice notes, and GPS.
- Getting files to the Mac: LocalSend works on both iOS and Android with no cloud. The agent says AirDrop and some share sheets can strip photo location, so check on day one that GPS survives.
- API privacy rule: only read-only calls from the Mac, with coordinates rounded to about 1 km, and no photo or audio uploads.

[trimmed for the transcript]

1 message not shown

Agent

The three public APIs all answered without a key. I used the 1 km radius around your coordinates and a 350 m radius for Overpass. The results change the idea ranking in a few places.

iNaturalist (no key, HTTP 200)
- There's enough data for Parkdex. Within 1 km there are 566 observations covering 329 species across all months.
- October alone is thin: only 18 research-grade species. At 2,500 m near the equator there's little seasonal pattern, so I'd drop the month filter and use all-year records.
- The top October results are butterflies and plants, not birds. Those are hard for Gemma to identify from a photo, so squares should stay category-level.

[trimmed for the transcript]

You

I have two Android phones. If you need more details in terms of resources and capacity, let me know

Agent

Two Android phones is a good setup for the sensor plan. LocalSend, phyphox and GPSLogger all run on Android, and you avoid the iOS restrictions on browser sensors and the light sensor. One caveat: Google Photos sharing strips location, so move the original files (from the camera's DCIM folder) to the Mac.

For each phone I'd need:
1. Make and model. That tells me what sensors it has, and many mid-range Androids have no barometer.
2. Android version and free storage.
3. Camera settings. Is "location tags" switched on in the camera app?

[trimmed for the transcript]

You

Make and model: Redmi Note 11, Freedom Factory DGen1
Android Version: 13, 15
Free storage: 10 Gb, 230 Gb
Camera settings: not sure

Agent

The Redmi Note 11 is fine as a capture device. I couldn't find any specs for the Freedom Factory DGen1, so I won't guess about it.

Redmi Note 11 (per GSMArena and other spec sites)
- It has a Snapdragon 680, 4 or 6 GB RAM, a 50 MP main camera, and an accelerometer, gyroscope, compass and light sensor.
- It has no barometer.
- It isn't a candidate for running Gemma on the phone. Google's E2B guide asks for 6 GB or more, and with a weaker chip you'd get slow output. This fits the plan that the phone only captures data and the Mac does the AI work.

[trimmed for the transcript]

You

let's do another brainstorming iteration. Let's include the following thoughts:

might include a way to establish a random route in the and min steps as goals when going out for a walk with the dog or going out explicitly to exercise. The more I move, the more goals achieved, the score increases. Weekly Score tiers unlocks the chance of treating well and buying sweets, snacks, or other food etc so the user has to earn eating them by completing the goals, or feel disappointed to eat something extra if otherwise
Spin up an agent on an open source harness like Hermes. Then, using an open-weights model (hosted not local) and connecting it to a messaging app like Telegram. Me as a user can make him a call and maintain a hands-free voice call around a topic:
- ai-generated meditation for when I'm driving
- ai-generated content related to a topic. I load and pick topics of discussion. Then the agents talks for a few minutes and test the user's understanding from time to time.

Agent

Round 3 is back from the brainstorming agent. It checked the facts that the voice and Hermes idea depends on, and the news there isn't great. It wrote no files, and I haven't verified its findings myself.

What it found about your Hermes and Telegram idea

Telegram bots can't hold a live voice call. The Bot API only sends voice notes and audio files. Real calls would need a "userbot" driving your personal Telegram account, which the agent ruled out as unrealistic in 5 days and risky for your account.
Hermes Agent is MIT licensed, has a single gateway for Telegram, Discord and others, has cron jobs, and connects to any OpenAI-compatible model endpoint.

[trimmed for the transcript]

You

look if anyone on DEV has spun up an voice-over agent using Hermes or similar through a messaging app like telegram, discord

Agent

Several DEV posts cover pieces of this. I found none that holds a live hands-free voice call through Telegram. The closest are Discord voice with Hermes, which is slow, and Telegram voice-note loops. I read 7 posts in full. Treat anything else I mention as title-only.

The closest matches

Hermes with live voice
- Hermes agent: Connect to Discord is the only real-time voice conversation with Hermes I found.
- The author joined a Discord voice channel and talked to the bot, and called the result "super slow".
- They say real-time conversation needs streaming, which Hermes doesn't have.

[trimmed for the transcript]

You

check Hermes docs, but also look for similar use cases using openclaw

Agent

Hermes's docs describe more than the DEV posts suggested, and OpenClaw has one thing Hermes lacks: a real phone-call plugin. I also found two new Week 1 entries that overlap with the photo and walk ideas.

Hermes docs

Telegram is voice-note only. It transcribes your voice messages and replies with voice bubbles. Hermes has no calls, and the voice page says phone calls and telephony are unsupported.
Continuous talk works in the command line, Discord voice channels, and a desktop app. The command line has push-to-talk. Discord voice channels listen continuously.

[trimmed for the transcript]

You

can you use gps data to calculate steps, distance, calories ?

Agent

Yes for distance, partly for steps, and only roughly for calories. Treat steps and calories as estimates.

Distance (good)
- Sum the great-circle distance between consecutive GPS points (the haversine formula).
- Raw GPS inflates distance, especially while you stand still or move slowly. A dog's sniff stops are exactly that case.
- Fix it in code by dropping points with poor accuracy and ignoring tiny movements (for example under 3 to 5 m between points). Light smoothing helps too.
- Tree cover reduces accuracy, and a local park has plenty.

[trimmed for the transcript]

You

let's go over another brainstorm iteration. Here are some resources and ideas that can be useful:

a Time article on AI and the real world
Ideas around an ambient agents using an open source harness like hermes and other parter tools
Build something on top of Jarvis
Idea: an agent that collects user thoughts offiline and build a second brain via sync when back online. Foster creativity by allowing a user to quickly save ideas that comes to his mind when outside. In order to reduce screen time, it could be voice over, like tap and record.
I just bought a ESP32 kit + MicroSD card module + HiLetgo GY-521 3 Axis Accelerometer Gyroscope Module 6 DOF 6-axis Accelerometer Gyroscope Sensor Module

Agent

Round 4 is back from the brainstorming agent. It wrote no files. The "checked" items below are its own claims, which I haven't verified.

What it found about your resources

ESP32 kit has no microphone, speaker or battery holder. It does have PIR, ultrasonic, temperature and light sensors, an LCD, buzzer, keypad and buttons.
- Recording audio on the ESP32 itself isn't possible without a mic module you don't have. Audio stays on the phone.
- The ESP32 can be a button, a beeper, a motion and light logger and a Wi-Fi uploader.

[trimmed for the transcript]

You

resources are not bound to each other (e.g. tap and record only via esp32 is wrong, tap and record on a phone. ESP32 for anything sensor-based). Brainstorm agent can come up with multiple ideas based on the list of resources. Given his report, is it needed to made amendments?

Agent

Round 5 is back from the brainstorming agent. It wrote no files, and the hardware details are its own and untested. One design rule applies wherever ESP32 logs meet phone files. The ESP32 has no real-time clock, so it takes network time over Wi-Fi before you leave.

The ideas

1. Idea Leash v2: capture thoughts outside, build the second brain later. Phone only; no ESP32.
- Capture: you record ideas on the phone's recorder or in a Telegram voice note on the walks. The Mac transcribes them (whisper.cpp) and files them as wiki notes (Hermes or plain Python, with local Gemma).

[trimmed for the transcript]

You

let's get into the Idea leash V2 project. Here are a list of additional features that could drive its design:

During my exercise walks at night I like to listen to podcasts. I'd like to be incentivized to walk and listen to podcast while getting tested of my understanding on the topics discussed. The workflow should be like:
- before going out, I give the agent the youtube link to the podcast episode. The agent downloads the audio version of it and process it to include main ideas, definitions, facts and insights into the 2nd brain wiki. Then prepares a 5 question quiz to test my knowledge after the exercise
- An offline-first PWA registers my walk, total distance and path taken (to be loaded in OSM) in the background. I can use a tap and talk or directly send an audio message in telegram that wait to be sent when back online
- After exercising, the agents test my knowledge and scores both time spent exercising and right answers. All gets registered as a session.

Agent

Idea Leash v2 is now the chosen project. I've marked it in AGENTS.md (with your podcast-quiz loop written out as the design brief) and logged the decision and supporting findings in docs/CHALLENGE-MEMORY.md. Nothing is built yet.

It's a big scope for the time left. I checked what's on your Mac and found six technical risks that shape the plan.

What's on your Mac

Already installed: Ollama, ffmpeg, Node 24, Python 3.14, uv. The hardware is an M4 Max with 64 GB RAM.
Models already pulled: Gemma 4 (e2b, e4b, 12b), gpt-oss:20b, qwen3.5:9b, glm-4.7-flash, plus embedding models.
Missing: yt-dlp, whisper.cpp, Piper (speech), Hermes and Syncthing.

[trimmed for the transcript]

You

Here are my answers:

English only
Redmi note 11
Yes
Yes, but try to install it at the project scope level

Other feedback:

Make sure you use an open source harness like Hermes for certain features instead of reinventing the wheel.
Do not forget to judge the whole idea around the challenge prompt and theme
Try to maximize parter tools usage so the project qualifies for some prizes. You can run an ideation and scoing agent using Opus 5.5 to carry this creativity process

Agent

The tools are installed inside the project, and the scoring agent's report is back. I've logged all of it in docs/CHALLENGE-MEMORY.md. Nothing is committed yet.

Set up inside `challenges/02-week1-touch-grass`

Python environment: a uv project pinned to Python 3.12 with yt-dlp, piper-tts and cmake. Nothing is installed globally.
whisper.cpp: built into vendor/ with the Metal backend, plus the large-v3-turbo-q5_0 model (547 MB).
Speech voice: Piper en_US-lessac-medium (60 MB).
Not installed: Hermes. The official installer writes to global paths, so I'll check whether it can install into the project first.

[trimmed for the transcript]

You

here's my feedback:

For the podcast audio transcription, explore using graphify. It already has a feature to transcript audio files and event extract youtube videos. It it can be integrated further for other features you may find interesting, propose it
Podcast content to be included in the demo are: Bankless, InTheLoop, and TheDeFiReport
How GPSLogger is going to be intregrated into the project? Having to run another app separately introduces friction
Create a system diagram artifact that includes the tech stack and how your ideas improves the project's idea alignment with the challenge theme
We should prioritize features that incentivize to get people off the screen and into the world with the help of AI and open tech
During development, Hermes will run locally within a VM. We should use Lume

Agent

The diagram is published as a private Artifact. It has the system map, a minute-by-minute walk timeline, the tech-stack table with measured versus planned status, and the theme-alignment argument with score estimates. Those scores are the brainstorming agent's estimates, not a judge's view. Only you can open the page unless you share it.

I also logged your decisions and findings in docs/CHALLENGE-MEMORY.md.

Answers to your feedback

[trimmed for the transcript]

You

here's my feedback:

Graphify deeper integration: sounds good, however, I think semantic extraction will be very slow on local models, so leave it as optional feature, while still having okf-wiki like content for the agent to get content from. Be critical on this fact I'm putting. Am I confusing features or right?
Podcast content: the project extract audio transcripts only for growing the second brain wiki and for creating the quiz. The podcast itself will be listened using the official app (e.g. Youtube Premium allows to download the episode and listened while the app is in the background)
What do you mean the okf-wiki skill predates the challenge window?
Let's use Tailscale for connecting devices via private VPN

Agent

Is semantic extraction too slow on local models?

You're right about semantic extraction, and I measured it:
- graphify's headless extraction with gemma4:12b-it-qat on a 1,724-word transcript took 604 seconds, then reported no usable nodes.
- That's one run on default settings, and I didn't try other models. An hour-long episode is about 6 to 8 times longer, so it can't sit on the critical path.
- Where I'd correct you: graphify's transcription is a different feature, and it doesn't use an LLM. It took 47 s for 699 s of audio on base. I'd keep that part.

[trimmed for the transcript]

You

here's my feedback:

It's fine to create a macOS VM for now
Keep the new version of the graphify skill
In the PWA, make the top-left corner text more visible while the screen is black, and put a "Record" button in the middle that triggers the voice notes
While the VPN is enabled, let's upload the test PWA to vercel

Agent

All four are done. One caveat: the real phone test (wake lock, GPS with YouTube playing) is still open.

Walk test page

The black screen now shows a clearly boxed "Hold 3 s to stop" corner with bright text. It turns amber while you hold and says "keep holding…". A big Record button sits in the middle. Tap it to start a voice clip and tap again to stop, with an elapsed-time line below it. I ran a full cycle in the browser pane with stubbed GPS and microphone: clip saved, report generated, no script errors. The pane denies the wake lock, so that part is the Redmi's job tonight.

[trimmed for the transcript]

You

Tailscale tailnet is setup in my mac and mobile
A SerpAPI key is already in .env
Telegram bot token and user id are setup in .env

Agent

Everything on your list checks out, with three small steps left for you. The VM is also still finishing its setup.

What I verified

.env: every key is filled in. I checked key names only and never read the values. It's gitignored.
Tailscale: it was installed but stopped on the Mac. I turned it on with your existing settings. Your Redmi is in the tailnet but showed offline.
Name privacy: the HTTPS certificate will publish the machine name in public certificate logs. It has no personal name in it, so I'm fine proceeding with it.

[trimmed for the transcript]

You

Tailscale Serve is ready to use
HTTPs certificates are enabled in the console
Tailscale is turned on on my phone

Agent

Both of the first two steps are done, and the VM is the one open problem.

Tailscale and the walk page

Serve works: the page loads from the Mac with HTTP 200 and a valid certificate. It's tailnet-only, so anyone outside it can't reach it.
Phone: it's online and answers tailscale ping. The Vercel page also works for tonight.
One reliability problem: the small server behind Tailscale had died once, which caused a 502 error. I restarted it. The real app needs a proper supervised server.

[trimmed for the transcript]

You

PWA app loaded from the Tailscale URL. I'll use Vercel in tonight's tests
Proceed with the features you can make progress. Regarding the quiz generation, would be a great to write a skill for that using the skill-creator so hermes or any harness uses it

Agent

The quiz skill is built, tested and valid. The VM exists but needs about five minutes of your input.

The quiz skill

It's in .agents/skills/podcast-quiz/, where Hermes, Claude Code and the local pipeline all read it. The skill-creator validator reports it as valid.
- Generate mode: a transcript goes in (plain text or SRT). Out come 4 spoken-friendly multiple-choice questions plus 1 "field link" question to answer outdoors, each with a verbatim transcript quote.
- Grade mode: it scores your spoken answers 0 to 2 with one sentence of feedback. Multiple-choice answers are graded by code.

[trimmed for the transcript]

You

I enabled remote login on the leash-hermes VM
Implemented skills should be put under a skills subfolder to then be installed either on .agents or hermes skills directory
Later when defining the leash agent behavior, check the docs on how to setup multiple profiles in Hermes. All config & definition files should be added to a folder in the challenge directory. While there should be instructions and even a setup script to easily setup the agent profile on an hermes instance

Agent

Both changes are in. One step needs you before I can continue with the VM.

Done

Skills: podcast-quiz now lives in challenges/02-week1-touch-grass/skills/, and the pipeline reads it from there. scripts/install-skills.sh installs skills in two ways:
- For Claude Code and other agents: --target agents creates a relative symlink in .agents/skills.
- For Hermes: --target hermes --profile leash copies it into that profile's skills/ folder and leaves the test files out.
Quiz check: the quiz still runs from the new location (2 attempts, 44 s).

[trimmed for the transcript]

You

I enabled the Local Network to Claude and the command you requested to run returned "open".
VM username: hermes

Agent

I'm setting up SSH access to the VM. One step is yours: type the VM password.

What I found

Your Local Network switch worked for the app's terminal panel, which reaches the VM (port 22 is open). My own shell is still blocked, probably until the Claude app restarts. I don't need that restart: I'll run VM commands through the panel, where you can watch them.
I opened a panel tab. It created a dedicated key and is now waiting for the hermes account's password. Please type it in that tab and press Enter. I won't see it, and I'm not asking for it in chat.

[trimmed for the transcript]

You

I entered the password, go ahead

Agent

Hermes is installing in the VM, and the Ollama link already works.

Where things are

SSH to the VM works. Your hermes user logs in with the key, and leash-hermes is an alias in your SSH config.
The tunnel works. The guest reaches your Ollama at [REDACTED]:11434 through a reverse SSH tunnel and lists the models. Ollama stays bound to the Mac's loopback only.
Hermes is installing in a tab. It's slow (the repo is about 120 MiB, plus Python 3.14, node, ffmpeg and browser tooling).

[trimmed for the transcript]

You

Hermes install finished, go ahead

Agent

The agent works end to end inside the VM, and it passed the first real tool-call test.

What I ran

Hermes v0.21.6 in the leash-hermes VM, with the profile leash on your leash-gemma4-12b model reached through the SSH tunnel. I asked it to make a quiz from an SRT transcript using the podcast-quiz skill, then run the verifier and the shuffle script. It did all of that on its own:
- Time: 3 minutes 14 seconds, on the first try.
- Verifier: "OK - 5 questions". I re-ran the verifier on the Mac against the file it wrote: exit 0, with correct answers in positions 2, 0, 1 and 3, so the shuffle ran.
- Limits: that's one run, so I can't say how reliable tool calls are across many runs.

[trimmed for the transcript]

You

Build the ingest step first and then define an initial version of the agent's behavior. I already have a few phone walk reports that I'll provide later with some details I found

Agent

The ingest step works on a real YouTube episode, and I've defined a first version of the agent's behavior. The agent isn't fully reliable yet, so I list what I measured below.

Ingest step

python -m leash ingest <link | audio file | transcript> takes a podcast and produces:
- Transcript (graphify's transcriber, with an optional faster whisper.cpp engine).
- Notes: main ideas, definitions, facts and insights. Each item must carry a quote that is actually found in its section, and unsupported items are dropped (2 were dropped on the real episode).
- Second-brain pages (OKF format: source page, concept pages, index, log and hashes).
- The quiz from the podcast-quiz skill, plus spoken audio for each question made with Piper.

[trimmed for the transcript]

You

go ahead with the telegram gateway setup
stay with gemma 12B. Bigger models do not work reliably in my Mac
I'll decide on the vault bootstrap and provide the walk reports later

Agent

The Telegram gateway is live, and it's ready for you to try.

What's running

Gateway: the Hermes gateway in the VM reports "Connected to Telegram (polling mode)". hermes gateway list shows your leash profile as "served by the default multiplexer", so it serves the profile without a separate process.
Why not -p leash gateway run: Hermes v0.21.6 allows only one gateway per host, which serves all profiles, so I started it from the default profile.
Model: it stays on the 12B, as you decided.

[trimmed for the transcript]

You

I started testing telegram bu got another kind of message when sent a "quiz me" message:

I'm sorry, but it looks like I can't reach the local tools on your Mac right now because a required connection token is missing. Please let me know if you'd like to try looking for a podcast or sharing an idea instead.

Agent

The bot should work now. I haven't confirmed it, though, because the fix needs a real message from you.

Cause: Hermes doesn't pass the profile's secrets to the shell tool. The bot's leashctl calls ran without LEASH_UPLOAD_TOKEN, so the tool failed. The model turned that error into the message you saw. The gateway log shows the exact error. My earlier tests didn't catch it because I had set the token by hand in the test shell.

Fix: I made two changes, both as the Hermes docs describe:
- terminal.env_passthrough in agent/config.yaml now lists LEASH_UPLOAD_TOKEN and LEASH_SERVER.
- The leash-tools skill now declares the token as a required variable.

[trimmed for the transcript]

You

I've got the following message when submitting quiz me one more time

I'm sorry, but it looks like the connection token is still missing, so I can't access your saved episodes to start the quiz. You might need to check your local setup to ensure the LEASH_UPLOAD_TOKEN is correctly configured.

Agent

This second failure didn't come from the token. The model never ran the command.

No request reached the Mac. The server log has no new entries.
No new tool error in the gateway log. The only new line is the old tool-call ID from your first attempt.
What happened: the 12B model reused the earlier failure from your Telegram chat history and answered from that instead of trying again. So the token fix is still unverified.

I added a rule to agent/SOUL.md telling the bot to re-run a command whenever an earlier error is in the chat, and I redeployed it to the VM.

[trimmed for the transcript]

You

I've got the first questions prompted. Make sure the agent uses telegram widgets to display questions an candidate answers

Agent

The quiz now runs through Hermes' own button tool, but it isn't verified yet. I haven't tapped the buttons in Telegram, and I don't know if the 12B model will call the tool reliably.

What changed
- Buttons: the quiz skill and SOUL.md now tell the agent to ask each multiple-choice question with the clarify tool. Telegram shows the four choices as tappable buttons, plus an "Other" option, and the correct answer is never shown.
- Open question: the last "field link" question stays a plain message, so you can answer it with a voice note.
- Tap handling: the server now accepts a tapped button's text, a number, or a letter, and still takes spoken phrases like "my answer is B".

[trimmed for the transcript]

You

the model asked in plain text instead of using buttons. the test ran a while ago and the host hibernated, so probably the vm, the ssh tunnel or the hermes gateway need a restart

Agent

The tunnel and gateway are back up and the quiz flow is changed. I haven't confirmed the buttons appear in Telegram, because that needs a real message from you.

Restarted
- Tunnel: it had died, so I restarted it. Ollama and the Leash server on the Mac were fine, and the VM could reach both through the tunnel.
- Gateway: it wasn't running in the VM. I restarted it, and the log shows "Connected to Telegram (polling mode)".
- Tool server: I restarted it on the Mac with the test vault.

[trimmed for the transcript]

You

here's my feedback:

I ran a full quiz session. It worked as intended. Check the responses in the logs if you can.
I tried sending voice messages like I'm driving, quiz me and a voice note with some ideas and the answer I've got from the agent were I'm sorry, but I still can't hear your voice message automatically...
I couldn't test the Send a voice note to check it's saved and sending a voice note asking to search something
the "sleep problem": yea, a startup script and launchd services will be good. Put them in the challenge directory with instructions on how to setup them
What project components are currently run on the host vs within the VM?

Agent

Everything you asked for is done except one item. Voice notes still need a real test from you, because I could only test the transcription path with an audio file on the Mac.

1. Your full quiz session

The server log shows one next call and one answer call per question, all returning 200. The scores were 1, 0, 1, 1, 2, which is 5 out of 6.

Latency is a problem for hands-free use. Ollama's log shows most turns taking 0.7 to 7 seconds. The first turn after idle took 37 to 50 seconds, and three turns near the end took 1m05s, 1m13s and 2m29s. I don't know the cause yet. All 183 agent requests went to your local Gemma through Ollama, so no hosted model was involved.

[trimmed for the transcript]

You

here's my feedback to your latest work:

Latency: you may see some longer time spent on certain quiz questions because I wasn't paying much attention, so some of them answered after a few minutes
I tested a few voice notes. Check out the server logs

here's my report on using the phone PWA (Vercel deployment) through different walking sessions

I ran 4 different walking sessions (1 to 4)
- Walk #1: I opened the app, enabled GPS and microphone and tap the "start walk" button while keeping the browser open and the phone screen alive during my walk.
- Walk #2: followed the same procedure as above but also started the podcast episode on YouTube before starting the session. At some point I mistakenly touched the screen inside my pocket and the phone switched to another app. When I returned to the browser, it requested permissions again and the running session was closed.
- Walk #3: I gave full gps/mic permissions, left the browser opened and turned off the screen to check whether the app kept the gps working while the phone screen is off.
- Walk #4: a walk with my dog, screen on, phone in my pocket.

Agent

The phone walk results changed the design, and the app is built and tested in a desktop browser. It is not yet on your phone. It needs Tailscale switched on first.

Latency

That explains the long gaps between your answers. It doesn't explain three slow model requests of 1m05s, 1m13s and 2m29s in Ollama's log, which are processing time for a single request. I logged that as an unexplained outlier.

[trimmed for the transcript]

You

Tailscale is on, go ahead with phone-serve and pairing

Agent

The QR code is in a terminal tab. I haven't read that tab's output on purpose, because the token is inside the code.

To pair
1. Open the Tailscale app on the phone and connect it.
2. Scan the QR code with the phone camera and open the link. The app should say "Connected to your Mac."
3. Start a walk, and allow location and microphone while visiting the site, not "this time".
4. When you're back, send me what happened, including whether the screen stayed on, any interruptions and how the resume worked.

[trimmed for the transcript]

You

The phone is paired and connected. I'll come back with a report after my exercise session

Agent

The QR tab was already closed, so there's nothing left to clean up. The token was never printed as text, and the code is gone from the screen.

The stack is running for your session: the tool server, the tunnel, the VM, the gateway and the published app. When you're back, send whatever you noticed: how the screen behaved in your pocket, any interruptions, and how the voice notes went.

You

I just came back a walking session using the app. Check the server logs for the walk

Agent

The walk is fully recorded and the new app held up. Here's what the Mac received.

The walk (20:33 to 21:09, podcast kind, screen on in your pocket)


Duration	35m17s, tracked 2116 s of 2117 s
Distance	3151 m as the Mac counts it (3329 m unfiltered)
GPS	4321 fixes at about 2 per second, median accuracy 6.9 m, none worse than 18 m
Interruptions	page hidden 0 s, 0 gaps, no errors
Battery	91% to 85%

[trimmed for the transcript]

You

the after-walk message sounds like a great addition to the ambient agent idea
should we keep the test-vault until I make another test tomorrow with the phone in offline-mode during the walk?

Agent

The after-walk message is built and tested everywhere except the real delivery to your chat. Tomorrow's walk will be the first real delivery.

The test vault

Yes, keep it until tomorrow's offline walk. Switching mid-test would only split your ideas across two folders.

How the message works

When a walk reaches the Mac, the server sends you one Telegram message, built by plain code with no model involved.

[trimmed for the transcript]

You

yes, send the test message

Agent

I sent the test message, and Telegram accepted it.

It is labelled "TEST MESSAGE (sample of the after-walk summary, not a real walk)" and uses sample numbers, so please don't read them as a real walk.

I logged this in the challenge memory as "accepted by Telegram", not yet as "seen by you".

[trimmed for the transcript]

You

in the meantime, get a brief summary of the implemented features and a list of the ones that are pending

Agent

Here is the state as of tonight, 2026-10-09, with about 51 hours left until the deadline.

Implemented

Area	What exists	Evidence
Ingest	A podcast link goes to audio download, local transcription, verified notes, a 5-question quiz and a wiki page.	A 30-minute episode took 477 s end to end.
Quiz	The `podcast-quiz` skill checks every question against a transcript quote and shuffles the answer positions.	27 of 27 checks passed, against 20 of 27 without the skill.
Quiz in chat	The server keeps your place in the quiz. Multiple choice shows as Telegram buttons.	A full session on your phone scored 5 of 6.

[trimmed for the transcript]

You

go ahead with the README, license and claims table

Agent

I've written the licence, README and claims table. The README's setup commands have not been run from scratch on a clean machine.

What's new

LICENSE: MIT, 2026, written from the standard text.
README.md: replaces the scaffold. It covers what the project does, why open matters using only what the logs show, a status table, run steps, a stack table with licences, honest limits, and credits and prior work.
Claims table in docs/CHALLENGE-MEMORY.md: 19 "may claim" rows, each tied to a log entry, with a matching "must not claim" for each.

[trimmed for the transcript]

You

let's improve the UX of the PWA. Make it more appealing and introduce icons or generated sprites to represents and compliment certain text labels like walk, dog, etc. Addtionally, you can include a menu bar to enble additional page views like activity stats and badges earned. For the latter, design some simple badge images. You can spin up a design and UX subagent using Opus 5.5 to assist you with this

Agent

The app now has three views (Walk, Activity, Badges), a bottom navigation bar, 20 icons and 16 badges. I tested it in a desktop browser against your real data. It isn't on your phone yet, and I haven't seen it there.

How the work was split

Design subagent (Opus): I started it with the opus alias, so I can't confirm the exact model version. It wrote the icon sprite, the badge sprite, a review page and a 700-line design spec. It did not touch the app.
Me: I built the /stats data it needed, checked its SVGs in the browser, and wrote the integration into the app.

[trimmed for the transcript]

You

I'm going out for a dog walk in a bit. Check everything is up and remind me what should I've been testing (e.g. offline mode)

Agent

The Mac side is ready for your walk. The one thing I can't see is the phone: Tailscale lists it as offline, last seen a few minutes ago.

What's up

Tailscale and the app: the app is published on your tailnet. The new version, the sprites and the manifest all load, and the API refuses requests without the token.
Services and model: the tool server, the tunnel and Ollama are running.
VM and gateway: the VM answers, the gateway serves the leash profile, and the tunnel works both ways.
Telegram: the bot token is valid (a read-only check, nothing sent).

[trimmed for the transcript]

You

I just got back from the walk, check the server logs:

Offline mode worked smoothly
Telegram message arrived after the walk
New design and UX is good
Two voice notes asked to do a web search. Should that triggered something?

Other feedback: there's should be a minimum walk distance requirement to save the session. Set it to 5 minutes

Agent

I've reviewed the server logs and made the 5-minute change. One question is left for you about search.

What the logs show

The walk: 11:40 to 11:59 (18m55s), a dog walk with the phone offline. 722 m, 1810 fixes, median accuracy 4 m, 1131 of 1135 s tracked.
Offline worked as designed: the walk reached the Mac about 30 s after you stopped, the three voice notes were transcribed within about 2 s each, and the Telegram message went out at 11:59:39.

Did the search notes trigger anything?

No. There was no POST /search in the log.

[trimmed for the transcript]

You

here's my feedback:

do the search-related notes using your recommended approach
Yeah, 5-minutes duration should be the requirement
I tested the search feature right from telegram using a voice note and it worked

Agent

Voice-search notes are done and tested, and I've confirmed the 5-minute rule is a duration requirement. The search path has not been used on a real walk yet.

How voice-search notes work

Trigger: the Mac acts only on a note that starts with an explicit phrase: "search for…", "search the web for…", "do a web search on…", "web search…" or "look up…".
Result: after you stop, the top 3 results arrive as plain titles and links in the Telegram message. A model doesn't write them.
Limits: at most 3 searches per walk, and it counts against the daily cap of 8.

[trimmed for the transcript]

You

give me an overview of all things needed to close this week challenge project

Agent

Here is the closing overview. You have about 33 hours left: the deadline is Monday Oct 12, 01:59 local (06:59 UTC).

Where things stand

Built and tested: the ingest pipeline, the quiz in Telegram, voice notes, the walk app with three views, offline walks, the after-walk message, voice search, the 5-minute rule and the services. All test files pass.

Not done: the post, the demo, the audit, and some housekeeping and decisions below.

[trimmed for the transcript]

You

Regarding the decisions table:

Still hold on the test-vault until the last offline test walk with podcast
Hold the commit until I report back the last test walk
Regarding YouTube terms, make sure to mention podcasts in general in the submission post. The yt-dlp usage stays in the README
Hold post draft writing and submission until I say so
I accept what you recommended for the other items in the table

Things I've done

Already record a demo during the test dog walk. I'll do another one with a podcast tonight. Final demo video should be edited by me by tomorrow morning tops.
I enabled automatic log in to the hermes VM

Your next actions

Everything you listed in Things I can do now except for vault promotion, post draft and staging

Agent

The closing items are done, and everything you put on hold is untouched. Nothing is committed or pushed.

What I did

Walking-route privacy: your raw GPX tracks stay local, and the tests use anonymised copies (rotated, moved about 3,800 km and re-timed).
Walk test evidence: the four report logs are committed as docs/walk-tests/*.txt, stats only.
Index rows: the challenge index and root README now say "Idea Leash", built and tested, demo and post pending, not published.
README final pass and feature freeze logged in the memory file.

[trimmed for the transcript]

You

make sure everything is up so I can test the app tonight. Make sure the mac stays up with caffeinate for two hours. That'll give me enough time until I come back. Then give me a quick sample message I can send in telegram to load a podcast via a youtube link

Agent

Everything is up for tonight, with one thing only you can do: connect the phone's Tailscale.

Status (19:55 local)

Mac awake: caffeinate runs for 2 hours. The Mac is on AC at 100%.
App and services: served over HTTPS at version 3, the tool server, tunnel and Ollama are running, and the VM and gateway answer.
Models: loaded and kept warm, so the first message won't be slow.
Search: 3 of 8 searches used today.

[trimmed for the transcript]

You

i just came back from my exercise session. Check the logs

Agent

The evening test mostly worked, and it also exposed four real defects, which I've fixed. The part I couldn't fix myself is that the 12B model told you a wrong answer was right.

What went right

Podcast prep: your link became a 56.7-minute Bankless episode, done in 18 min 10 s, about a third of its length.
Walk: 34m19s, 2,863 m, 4,149 fixes, median accuracy 5 m, 2,059 of 2,061 s tracked, no gaps.
Quiz: all five questions were served with buttons and graded: 3 of 6.
Latency: agent turns had a median of 2.3 s and a maximum of 65 s, with no multi-minute turns.

[trimmed for the transcript]

You

Do both: send grading directly and notify when ready

Agent

Both are built, tested with a fake Telegram, and deployed. Neither has been seen on a real phone yet.

What changed

1. Grades are sent by the Mac itself
- Each answer produces a Telegram message built by code, for example Question 2 of 5: Not quite. The answer was: The pace is too fast.
- The last answer carries the total in the same message: Quiz finished: 3 out of 6.
- Fallback: if Telegram can't be reached, the old behaviour returns, with exact text for the agent to send. That case is tested too.

[trimmed for the transcript]

You

how should I retake the quiz to record it for the demo? And what's the criteria to get a good grade on open question #5 ?

Agent

Retaking the quiz for the demo

You need one reset per take, because last night's attempt counts as finished. Sessions roll over at 19:00 local (midnight UTC), so before then "quiz me" would just say it's done.

I changed restart so it archives your previous attempt instead of deleting it. In Telegram, send /new and then Quiz me.

[trimmed for the transcript]

You

help me make sure everything is up on my laptop. I want to upload the last offline walking session

Agent

Everything on the Mac is up and waiting for the walk. The phone is the missing piece.

Mac side

Sleep protection: a fresh caffeinate for 2 hours. The Mac is on AC.
App and services: served over HTTPS, the tool server, tunnel and Ollama are running, the VM answers, and the gateway serves the leash profile.
Model: warm, so the first reply isn't slow.

[trimmed for the transcript]

You

The walk is uploaded, check the server logs

Agent

The upload worked end to end, and the walk contains a real interruption that the app handled correctly.

The walk (dog walk, 10:44 to 11:08)

Duration: 23m53s. Distance: 921 m (counted by the Mac). 2,114 fixes, median accuracy 5 m.
Tracked: 1,290 of 1,433 s. 143 s are untracked, in one gap.
Errors: none, and no permission prompt.

[trimmed for the transcript]

Prize Categories

Gemma: Gemma 4 12B QAT, served by Ollama on my Mac, writes the notes, the quiz and the grading wording, and is the model behind the Telegram agent.
SerpApi: voice-requested web search. A walk note that starts with "search the web for..." runs one search after the walk and puts the top three links in the Telegram message (the example above); a Telegram voice note can ask too. The key stays on the Mac, and a daily cap lives in code.

What I Built

Demo

Code

santteegt / HacktoberFest2026

Hacktoberfest 2026

Challenges

How I Built It

Two agent skills

Where it ended up

Why Does Open Innovation Matter?

My Agent Session

Native Android with Google's LiteRT-LM (the most direct route)

What it found about your Hermes and Telegram idea

The closest matches

Hermes docs

What it found about your resources

The ideas

What's on your Mac

Set up inside challenges/02-week1-touch-grass

Answers to your feedback

Is semantic extraction too slow on local models?

Walk test page

What I verified

Tailscale and the walk page

The quiz skill

Done

What I found

Where things are

What I ran

Ingest step

What's running

1. Your full quiz session

Latency

The walk (20:33 to 21:09, podcast kind, screen on in your pocket)

The test vault

How the message works

Implemented

What's new

How the work was split

What's up

What the logs show

Did the search notes trigger anything?

How voice-search notes work

Where things stand

What I did

Status (19:55 local)

What went right

What changed

Retaking the quiz for the demo

Mac side

The walk (dog walk, 10:44 to 11:08)

Prize Categories

Set up inside `challenges/02-week1-touch-grass`