GitHub AI Daily · 2026-10-31 (Saturday)

1. Today's picks (5)

1. can1357/oh-my-pi: the IDE is now genuinely wired into the coding agent — the 37,251-star pi fork ran 74 commits in 24 hours, and today's theme is "make the agent guess less, measure more"

URL: https://github.com/can1357/oh-my-pi ｜ ★37,251 ｜ MIT ｜ TypeScript + Rust (~80k lines of Rust core) ｜ A pi fork by Stencil Labs with 60+ model providers, 31 built-in tools, 28 DAP operations and 14 LSP operations

What happened

This is one of the highest commit densities of the day: 74 commits across 10-30 and 10-31, with v18.14.1 released at 00:39 on 10-31. The commits look trivial in isolation, but strung together they point in one unmistakable direction — making the agent stop guessing about context and start measuring it.

• An entire morning was spent fixing "title drift": feat(coding-agent): judge title drift before refreshing after prompts → fix(coding-agent): keep user-set titles and redact title drift judgments → refactor(coding-agent): opt-in title drift checks that consume their prompts, redact native .... The logic is: after a turn the agent wants to refresh the session title, but it must first judge whether the title actually drifted, and a title the user set by hand must never be overwritten; because the judgement itself consumes prompt, it is made opt-in, and its verdict is redacted on display.
• The image budget is now specified to the byte: feat(catalog): raise Anthropic inline image budget to 120 MB for the 128 MB Messages limit → fix(catalog,coding-agent): keep Anthropic inline images within the 128 MB request body → fix(coding-agent): measure tool arguments as the string the Responses wire sends. Measure by the string that actually goes out on the wire, not by the internal object — that is exactly how they fill the 120MB Anthropic budget and still keep 8MB of headroom.
• A release within a single day: at 00:37 on 10-31 a Windows path-handling fix landed (walker stat and the rsync filter merge file), and at 00:39 came chore: bump version to 18.14.1.
• Two other entries worth noting: a new providers.openai-codex.daybreak setting that allows opting out of Daybreak Blue per session; and fix(agent): removed intent length rejection.
• The repository currently has PRs temporarily open to everyone (the README states the previous vouch requirement is lifted while they evaluate how open contributions go).

Why it matters

• "Judge whether the title drifted before refreshing it, and never overwrite a title the user set" matters more for my site than its star count suggests. When I build the AI assistant for 4pwellness or generate reports for the nutrition platform, the exact same problem shows up: the agent renames a session to something that "looks more on-topic", but the name the user typed is the one carrying business meaning (e.g. "冀敏 2024 baseline plan, phase one"). oh-my-pi splits this into three steps — judge first, then decide whether to refresh, then redact on display — and because judging consumes prompt, it defaults to off and the user opts in. That three-piece contract (opt-in + never overwrite user settings + redact the verdict) is something I can copy directly into the interaction design of the AI assistant on the 4P supplement platform.
• "Measure by the string the wire actually sends, not by the internal object" is a trap I would have fallen into. When I build lab-report OCR → AI → App import, my first instinct was to judge "whether the file fits in context" by file size — only to discover that what really decides success is the length of the final string spliced into the request body (base64 inflates, escaping lengthens). They moved the measurement basis from "object" to "what the wire sends" and thereby set the usable budget at 120MB under a 128MB cap — that is where the "keep 8MB spare" figure comes from; it is not a guess. This is a directly usable engineering conclusion for setting safe thresholds when I batch-import multimodal content (report images plus lab sheets).
• 37,251 stars, 74 commits and a release a day tell us one thing: the coding-agent foundation has moved from "does it work" into "are the edge cases handled cleanly". Last issue I noted LangChain core 1.7.3 alongside AutoGen going unmaintained and wrote that "the answer to which agent framework to build on is narrowing." Today's entry pushes that judgement one step further: competition at the foundation layer has shifted from "is the abstraction good" to "are these edge cases handled" — title drift, image budgets, byte-level measurement, Windows paths — none of which is about model capability; it is all engineering detail. For a one-person company with eight hours a week, that is good news: differentiation does not lie in training a model but in cleaning up the boundary cases.
• MIT licensed: readable, modifiable, commercially usable, with no copyleft burden for a product meant to be sold.

2. ayuayue/PiDeck: the Chinese community packs both pi and DSH backends into one desktop workbench — and DSH is the very harness running on my machine

URL: https://github.com/ayuayue/PiDeck ｜ ★1,170 ｜ MIT ｜ Electron + TypeScript ｜ An open-source desktop workbench that unifies pi and DSH (DeepSeek Harness) sessions inside local project directories

What happened

45 commits across 10-30 and 10-31, including an explicit chore: release v0.9.9 at 06:36 on 10-30, and the README changelog moved to v0.9.9 the same day (the GitHub Releases page still shows v0.9.8 from 10-27; the v0.9.9 tag has not been pushed yet — the distinction is recorded here as-is). Today's commit density comes from two main lines: filling out ACP (Agent Client Protocol) session capability, and consolidating multi-device and commercialization.

• All day on ACP, plugging other agents in: feat(acp): new Copilot CLI preset (copilot --acp), feat(acp): new MiMo Code preset (mimo acp), feat(acp): new Kilo Code and Cline presets, feat(acp): model list shows magpie multiplier and model description, feat(acp): sessions gain todo and subagent display (derived from tool calls), feat(acp): restore bypass permission level after history session recovery, feat(acp): collapse the footer when there are too many parameters, and auto-create plus warm up a session when the guide page selects ACP.
• Two DSH-side commits land squarely on my interests: feat(thinking): port the thinking-signature display sanitisation from DSH rc.5 — sanitising the thinking signature before display, the key step that prevents chain-of-thought bleed across multi-turn conversations; and fix(dsh): create the target directory before npm pack, fixing the inevitable spawn npm ENOENT on user plugin install — fixing a bug that made plugin installation always fail.
• One fix that shows real craft: fix(composer): text submitted with a Chinese IME no longer drops out of the draft (send key often grey / prediction grey text overlapping). A Chinese IME keeps the text in a pre-edit (composition) state, which swallows one keystroke; without special handling, the Chinese text a user types gets lost from the draft — a trap every desktop app with a Chinese input box walks into.
• Commercialization consolidation and security: refactor(ui): commercialization unification wave 2 — shared mode freeze, fix(ui): commercialization unification wave 1 full rollout — P0 security gaps, fix(config): clean illegal pi schema values before saving models.json, fixing issue #458 where the whole provider column vanished.
• EasyTier was upgraded to a manageable channel (system service start/stop plus phone configuration delivery), and external network access gained a read-only channel.

Why it matters

• "Port the thinking-signature display sanitisation from DSH rc.5" is a problem I have been running DSH v0.1.0-rc.6 against without ever thinking it through. I use DSH for native agent development, and the thinking signature is what guarantees the model "remembers how it thought last turn" — but rendering it directly is both a leak and a cross-contamination risk. They turned it from a port into a sanitisation step at the rendering layer, and that is the right place for it: sanitise before display, not at the model-call layer. This maps onto what I wrote last issue about hashgraph-online/hol-guard — "the defence line should be as early as possible" — except here what is being defended against is leaking into the user's own eyes, not leaking outward.
• PiDeck proves that "one desktop shell hosting multiple agent backends" is a shape someone is willing to build, and able to sustain. The approach is notably restrained: the README states plainly that "PiDeck is not a fork of pi. It is a lightweight Electron shell that launches multiple pi --mode rpc processes… all agent capability is provided natively by pi, session files are read and written natively by pi, PiDeck neither replicates nor hijacks." "Neither replicates nor hijacks" is my first criterion when evaluating any agent-ecosystem tool — because the moment you start replicating upstream capability, every upstream release becomes your maintenance burden. My GLP-1 Nutrition Companion V2 and the 4P precision-nutrition platform (port 8899) use the same "thin shell plus independent data layer" architecture, and PiDeck shows this path is viable in the Chinese community and attracts commit density of 45 a day.
• "Commercialization unification wave 1 full rollout — P0 security gaps" and "shared mode freeze" are directly actionable for the GLM Nutrition Companion V2 I am building. A product involving 153 ingredient templates, 15 functional targets and Excel export formulas is usually blocked not by features but by "which switches are we willing to let users touch". They pushed "wave 1 full rollout" and "wave 2 freeze" as two separate phases, splitting "fill the security gaps" from "freeze the shared modes" instead of mixing them into one change — a rhythm worth copying.
• Those three ACP presets (Copilot CLI / MiMo Code / Kilo Code plus Cline) mean the cost of adopting a new agent has fallen to "add a preset". For my "certification hook plus nutrition-AI kernel" lightweight assistant, this is good news: it means my product may never need to implement an agent runtime at all — it only needs to own the deterministic assets in the nutrition domain (ingredient templates, target mappings, contraindication rules). MIT licensed, Electron stack, no conflict with my existing toolchain.

3. OpenAdaptAI/OpenAdapt: turning "the agent says it succeeded" into a falsifiable claim — on an open-source EHR, 30 deliberately corrupted records were caught by read-back verification

URL: https://github.com/OpenAdaptAI/OpenAdapt ｜ ★1,854 ｜ MIT ｜ Python 3.10–3.12 ｜ An open-source "verified execution layer" for computer-use agents: compile one human operation into a program, then let the agent call the program as a tool

What happened

The problem this project tackles is very specific, and the numbers it provides are very hard. 13 commits across 10-30 and 10-31, almost all of them hardening the read-back verification.

• The core mechanism: a human (or the agent) performs a task once in a web, Windows, macOS, Linux or remote-desktop app; OpenAdapt compiles that run into a program, and the agent subsequently calls the program as a tool. Two properties matter here: the program makes no model calls and sends no screenshots anywhere; before each save it confirms which record it is about to write, and after the save it reads the value back from the system of record, returning only a plain outcome; any mismatch makes it stop and tell the agent rather than guess.
• Measured on OpenEMR (the open-source EHR whose 7.0 release is ONC-certified), from a fresh browser through sign-in, entry, and reading the saved record back — medians: agent alone (Claude Sonnet 5.5 computer use) 36.5 s, correct in 62 of 63, model cost about $0.08; OpenAdapt program with screen steps 22.5 s, 18 of 18 correct, $0 model cost; OpenAdapt program with an API step 3.3 s, 18 of 18 correct, $0 model cost. The API step replaces the program's 41 screen steps with the single form submission the app itself sends, then reads the record back.
• The single most important number: on an OpenEMR copy with faults injected into its database (a field blanked, a value changed, a duplicate chart, a chart removed, another chart deleted or edited), the agent claimed success on all 30 bad saves; read-back caught 25 of the 30 and confirmed all 5 clean saves, with no false alarms. On MockMed — a test app that shows a save as done before its server answers — the agent likewise claimed all 30 bad saves, and read-back caught all 30.
• A second open system: on a test copy of openIMIS (the open-source insurance and claims system behind Nepal's national health insurance scheme), the compiled program entered 149 synthetic claims with no model calls: 148 read back correct and none saved wrong.
• Today's commits are almost entirely about the verifier: fix(verifier): compare a stored number with an expected number by exact value (numbers must be compared by exact value), Verifier: a None parameter matches a stored null instead of making the read-back unre... (None should match a stored null instead of making the read-back fail), Verifier: SQL guard refuses placeholders in literals, locking reads and more s..., Verifier: tokenize the read-only SQL guard instead of matching substrings (the read-only SQL guard is now lexically tokenized rather than substring-matched), and Whole-database frame and recheck on PostgreSQL (the whole database enters the frame, rechecked against PostgreSQL).

Why it matters

• This project hits precisely the problem I have been trying to solve, and solves it harder than I do. When I build lab-report photo OCR → AI → App import, what worries me is whether the AI will fabricate a supplement-plan indicator value out of nothing. My approach is a validation layer inside the application; they elevated it into an architectural invariant — "must read back after saving" — and proved its necessity with 30 deliberately corrupted records. The number that should worry me most in that set is that the agent claimed success on all 30 bad saves — in other words, on an interface that only shows model output, the error rate appears to be 0%. That is far more severe than "the AI fabricates an indicator", because it leaves no chance to even notice an error.
• "Move the verifier's SQL guard from substring matching to lexical tokenization" is exactly where I would get my own comparison logic wrong. When I judge "was this lab sheet actually written into the database", my first implementation also matched SQL as a string — but substring matching is fooled by comments, string literals and nested clauses. After switching to tokenization the guard becomes reliable. Likewise three more: numbers must be compared by exact value (string equality is not enough), None matches a stored null (otherwise the read-back fails spuriously), and reject placeholders inside literals (injection defence). Those four together are a read-back verification checklist I can copy directly, and I intend to use it to audit the intervention-report write path on the 4P supplement platform.
• The "compile into a program, zero model calls and zero outbound screenshots at runtime" shape matters enormously for handling real health data. My 4pwellness platform has to process users' physical exam reports and lifestyle questionnaires, and once that data enters a model context and goes out with the request, PHI has left the building. OpenAdapt's architecture offers a more thorough answer: the part that must touch the data is frozen into a program that makes no model calls and sends no screenshots, and the model only decides which program to invoke. That is far more reliable than writing "please do not transmit patient information" in a prompt — the former is a structural guarantee, the latter is asking the model to behave.
• The cost comparison of $0.08 vs $0 and 36.5 s vs 3.3 s speaks directly to my "eight hours a week plus one month of cash runway" constraint. Every call in a one-person product is a real cost, and the marginal cost of compiled execution approaches zero. MIT licensed and Python-based, fully compatible with my Flask + SQLite system on port 8899.

4. meltforce/FreeReps: gather five wearable data sources into a self-hosted server, then hand them to an LLM over MCP — and today it fixed two Go vulnerabilities and an MCP SDK upgrade

URL: https://github.com/meltforce/FreeReps ｜ ★21 ｜ MIT ｜ Go + Vue ｜ A self-hosted health and training data server that collects from Apple Health / Oura / Withings / Hevy / Alpha Progression, visualises it, and exposes it to LLMs as an MCP server

What happened

Only 21 stars, but what it does is precisely the piece I have been looking for architecturally. Three commits on 10-31 01:44 and 10-30 — they look trivial, yet they land exactly on what matters most to this project right now:

• 10-31: fix(deps): update module tailscale.com to v1.104.4.
• 10-30 16:48: deps: golang.org/x/net v0.62.0 for GO-2026-6860 and GO-2026-6866 — patching two Go vulnerability advisories at once (GO-2026-6860 / GO-2026-6866).
• 10-30 01:44: fix(deps): update module github.com/mark3labs/mcp-go to v1.4.0 — upgrading the MCP Go SDK to 1.4.0.
• Looking further back: on 10-25 there is security: Go 1.27.3 (GO-2026-6809), plus ci: pin the reusable workflow to ci-workflows@v7 and ci: rebuild weekly on Go 1.27.2 and block on govulncheck (CI blocks on govulncheck, so vulnerable dependencies cannot enter the trunk). Two consecutive weeks of paying down security debt says this project takes handling real health data seriously.
• Its capabilities are worth recording on their own: first, multi-source collection (Apple Health, Oura, Withings, Hevy, Alpha Progression) persisted locally; second, a correlation explorer — any two metrics as a scatter plot with an overlay, yielding Pearson r and r recomputed at four lags from the same payload; third, overnight sleep staging, HRV and SpO₂ curves, heart-rate zones against a maximum the user sets or FreeReps estimates from a date of birth, strength-session sets, and GPS routes; fourth, an MCP server interface handing all of it to an LLM; fifth, an iOS app already on the App Store (version 2.2) that syncs only HealthKit deltas and can trigger a sync from a widget or Siri.

Why it matters

• "Correlation explorer: scatter any two metrics, give Pearson r, and recompute at four lags" is the thing I have wanted and never found implemented in the analysis of 4P precision-nutrition interventions. My current analysis is a three-way trigger judgement — improved / unchanged / deteriorated — across blood glucose, HbA1c, lipids, liver function, vitamin D, iron and ferritin, uric acid, weight and waist. What it fundamentally lacks is the layer of "is there a temporal correlation between two indicators at all." Take "eight weeks after vitamin D supplementation, is there a lagged correlation between ferritin change and HbA1c change?" — their four-lag recomputation is precisely the engineering answer to that question. MIT licensed and a Go backend, so I can borrow the algorithm without adopting the whole stack.
• The "deltas only" design detail shares a lineage with the "trustworthy assets" note I made about Q00/ouroboros last issue. On iOS it sends only what HealthKit added since the last sync, which both saves traffic and gives "when did this data enter the system" an unambiguous boundary. The same boundary question will certainly surface in my 4pwellness WeChat-group customer-service Bot and in GRPM nutrigenomics report import: the user switches phones, half the data is re-synced, the timezone changes, and the same indicator appears twice. "Delta sync plus explicit provenance" should be a uniform convention across every health-data entry point I build.
• "CI blocks on govulncheck, so PRs with vulnerable dependencies cannot pass" is one hard rule I should copy into my own projects. I run sync-backend on a Tencent Cloud Lighthouse instance (public IP 101.42.26.85) and a Flask platform locally, and dependency upgrades have always relied on my memory. FreeReps automates this with one CI rule: "a dependency carrying a GO-2026-6860-class advisory simply cannot get through." For a one-person product to be delivered inside a one-month cash runway, that rule protects the product better than two extra features.
• It also demonstrates how to read a project's seriousness about data trustworthiness: look at how it treats dependency vulnerabilities. Two of three commits are security fixes, which is unusual among the health-oriented open-source projects I have seen — most of them keep your data on someone else's server and charge a subscription. The combination of 21 stars, MIT, self-hosted and MCP-exposed is a minimal architecture worth copying for the "my personal health data stays mine" proposition.

5. deeplethe/sandstorm: turning "one sandbox per agent" into "thousands of sandboxes per machine" — 34 commits bring Firecracker and gVisor into one scheduler

URL: https://github.com/deeplethe/sandstorm ｜ ★3,246 ｜ Apache-2.0 ｜ Rust ｜ Run thousands of AI agent sandboxes on the cheapest machines: pause idle sandboxes and snapshot them to disk, restore them quickly when a request arrives

What happened

34 commits across 10-30 and 10-31. Today's batch pushes it from "a concept" to "a measurable thing".

• The core mechanism: idle sandboxes are paused and snapshotted to disk, then restored quickly when a request arrives, so one machine can hold far more sandboxes than it could run at once. Two backends are supported — gVisor containers and Firecracker microVMs — each sandbox has its own network namespace, and both backends can run in one fleet, each forming a resource pool of its own; the master serves a web console for viewing and managing sandboxes and scheduling across the fleet.
• Today's commits are almost entirely about "making it actually run": feat(sdk-python)!: drive Sandbox through the master's API (a breaking change consolidating sandbox scheduling onto the master's API), fix(node): remove a gVisor container that failed to start (cleaning up gVisor containers that fail to start, so they do not linger and hold capacity), fix(firecracker): let a restored guest through before setting its clock (a restored guest must be let through first and have its clock set afterwards — reversing the order causes restore failures), bench: latency under gVisor, KVM and PVM on one build (measuring gVisor, KVM and PVM runtime latency side by side on a single build), docs: a tidier tiers table, and what was done for each backend (documenting in a tiered table how far each backend has got), docs(sdk-python): a program that fails to load now raises.
• License and form: Apache-2.0, written in Rust, with a Chinese README (README-zh.md); the CI badges carry both gVisor and Firecracker.

Why it matters

• "Let it through first, set the clock second" is exactly the ordering trap I would hit writing a sandbox myself. I run sync-backend on a Tencent Cloud Lighthouse instance (public IP 101.42.26.85) and plan to deploy more agent services there. A guest restored from a snapshot usually has the wrong clock — and if you set the clock before letting it through, it may trip an internal clock-consistency check and hang. Letting it through first and setting the clock afterwards is the ordering that actually works. Without someone else having hit it and writing it into a commit message, I would have burned half a day debugging.
• "Measuring three runtimes side by side on one build" is directly valuable for my cost modelling. I need to decide, under a "one-person company plus tight budget" constraint, whether agent sandboxes should use gVisor (lightweight, shared kernel), Firecracker (microVM, strong isolation, slow start) or plain KVM. Their method is not one benchmark per runtime but locking a single build version and comparing latency across them — the only way to get a comparable conclusion. I will treat that table as the selection input rather than testing ad hoc myself.
• "Pause idle sandboxes and snapshot them to disk" maps directly onto my cost structure. What I care about has never been "how fast is a single sandbox" but "how many concurrent sessions can the same machine serve." Last issue I worked through lightpanda's numbers along these lines (1/16 of Chromium's memory → 16× more sessions on the same budget), and today sandstorm does the same thing along a different axis: not making each agent lighter, but making an idle agent stop occupying the machine. For my nutrition-AI kernel — daytime advisory work for clients, batch processing at night — "wake on demand, snapshot when idle" means I no longer have to pay for peak capacity year-round.
• Apache-2.0 (commercially usable, distributable under closed source, no copyleft contagion) plus Rust plus a Chinese README make the project noticeably more accessible to Chinese developers than comparable English-language efforts. And the architecture of "two isolation backends running in one fleet, each with its own pool" incidentally offers a multi-tenant isolation idea — more room for imagination than "one container per customer" when a single person's product has to serve many customers.

2. Category roundup

① AI Coding / programming agents and skill ecosystems

② Medical / clinical research AI and agent skill libraries

③ Health management / nutrition and food AI

④ Model foundation and agent infrastructure

3. Trends today

The single trend most worth recording today is that the centre of gravity in the AI agent ecosystem is shifting from "can it run at all" to "are the edge cases handled cleanly" — and that shift is happening simultaneously on all four of our lines. The 37,251-star oh-my-pi is fixing title-drift judgement and measuring bytes by the string that actually goes out on the wire; the 1,854-star OpenAdapt is fixing "numbers must be compared by exact value", "None must match a stored null" and "the SQL guard must be tokenized, not substring-matched"; the 1,170-star PiDeck is fixing "text submitted with a Chinese IME no longer drops out of the draft" and "DSH user plugin install always spawn npm ENOENT". Not one of them is about model capability — all of them are about engineering detail. For a resource-constrained one-person company, that is a clear signal: differentiation does not lie in training models but in cleaning up boundary cases.

• Verification is becoming a first-class citizen of agent architecture rather than an add-on. OpenAdapt's numbers are the most persuasive evidence of the day: with 30 bad saves deliberately injected, the agent claimed success on all 30, while read-back caught 25 on OpenEMR and all 30 on MockMed, with no false alarms. In other words, on an interface that only shows model output, the error rate appears to be 0%. That conclusion matters more than any "the model can achieve X%" capability claim — it means the way we have been evaluating agents may systematically overestimate reliability. I intend to write "must read back after saving, and the read-back verification itself must pass the tokenize / exact-value / null / injection checks" into the design conventions for the 4P supplement platform and GRPM report write path.
• The MCP ecosystem is entering a dependency-governance phase rather than a pure "plug it in" phase. FreeReps today moved mcp-go to v1.4.0, fixed GO-2026-6860/6866/6809 across two weeks and blocked govulncheck in CI; PiDeck worked on ACP presets, thinking-signature display sanitisation and freezing commercialization modes; oh-my-pi's fix(ai): key steering refusals by credential and record them only after a steer manages credential-rotation boundaries across repeated retries. Three projects on three different stacks, all handling the same class of problem: where exactly is the boundary when an agent holds credentials and permissions and repeatedly calls an external service. Every one of these traps is waiting for me too, as I connect MCP connectors to 4pwellness and build my own MCP server.
• The Chinese developer community's presence and output density in the agent toolchain are both rising. PiDeck's ACP adoption of Copilot CLI / MiMo Code / Kilo Code / Cline, its thinking-signature sanitisation and its Chinese IME fix; sandstorm's Chinese README and dual gVisor/Firecracker backends; and dense Chinese contribution across high-star projects like oh-my-pi, claude-skills and archify. One judgement: the Chinese-language agent toolchain is moving from "following the English community" towards "native implementations driven by local scenarios" — which is good news for products serving Chinese users, and for Chinese-language health content in particular.
• Security and cost landed on the same day, and they point at the same thing. FreeReps patching vulnerabilities and gating govulncheck in CI; oh-my-pi setting the Anthropic inline image budget at 120MB (keeping 8MB headroom under the 128MB cap) and measuring by the real wire string; sandstorm benchmarking gVisor/KVM/PVM latency and snapshotting idle sandboxes to disk. Taken together these three threads say one sentence: the deciding factor in this phase is reliable throughput per unit of cost, not how impressive a one-off demo looks. My "certification hook plus nutrition-AI kernel" minimum viable product needs its evaluation criteria changed accordingly.
• One thing to stay alert to: eight of today's entries are low-star projects (★0–★176), six of them MIT or Apache-2.0. That is exactly the band I care most about — information density far exceeding star count, clean licensing, readable or directly usable. But honesty is required: low stars mean no community validation, and shadowlzt/ironclaw-ai-vision and haoruilee/awesome-agent-native-services have no LICENSE file at all, so terms must be settled before citing them. Reading their code is fine; using them as a foundation means resolving licensing first.

4. Repo index

• can1357/oh-my-pi — https://github.com/can1357/oh-my-pi
• ayuayue/PiDeck — https://github.com/ayuayue/PiDeck
• OpenAdaptAI/OpenAdapt — https://github.com/OpenAdaptAI/OpenAdapt
• meltforce/FreeReps — https://github.com/meltforce/FreeReps
• deeplethe/sandstorm — https://github.com/deeplethe/sandstorm
• farion1231/cc-switch — https://github.com/farion1231/cc-switch
• nextlevelbuilder/ui-ux-pro-max-skill — https://github.com/nextlevelbuilder/ui-ux-pro-max-skill
• tt-a1i/archify — https://github.com/tt-a1i/archify
• alirezarezvani/claude-skills — https://github.com/alirezarezvani/claude-skills
• Q00/ouroboros — https://github.com/Q00/ouroboros
• Gentleman-Programming/gentle-shell — https://github.com/Gentleman-Programming/gentle-shell
• sno-ai/sno-station — https://github.com/sno-ai/sno-station
• jmfederico/pi-web — https://github.com/jmfederico/pi-web
• OHIF/Viewers — https://github.com/OHIF/Viewers
• Slicer/Slicer — https://github.com/Slicer/Slicer
• InsightSoftwareConsortium/ITK — https://github.com/InsightSoftwareConsortium/ITK
• fo-dicom/fo-dicom — https://github.com/fo-dicom/fo-dicom
• medic/cht-core — https://github.com/medic/cht-core
• avinashkranjan/lifeMEDeasy — https://github.com/avinashkranjan/lifeMEDeasy
• mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills- — https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-
• simonEllershaw/llm-discharge-summaries — https://github.com/simonEllershaw/llm-discharge-summaries
• thetahealth/mirobody — https://github.com/thetahealth/mirobody
• OpenStrap/edge — https://github.com/OpenStrap/edge
• Lethe044/hermes-life-os — https://github.com/Lethe044/hermes-life-os
• brandonp2412/FitBook — https://github.com/brandonp2412/FitBook
• shadowlzt/ironclaw-ai-vision — https://github.com/shadowlzt/ironclaw-ai-vision
• fitguyfitguy/Chompass — https://github.com/fitguyfitguy/Chompass
• Regevba/FitTracker2 — https://github.com/Regevba/FitTracker2
• Teamz-Lab-LTD/device-gpt — https://github.com/Teamz-Lab-LTD/device-gpt
• jaronskijakub/fatsecret-mcp — https://github.com/jaronskijakub/fatsecret-mcp
• flashinfer-ai/flashinfer — https://github.com/flashinfer-ai/flashinfer
• lemonade-sdk/lemonade — https://github.com/lemonade-sdk/lemonade
• kserve/kserve — https://github.com/kserve/kserve
• gpustack/gpustack — https://github.com/gpustack/gpustack
• tastyeffectco/sandboxd — https://github.com/tastyeffectco/sandboxd
• desplega-ai/agent-swarm — https://github.com/desplega-ai/agent-swarm
• agentic-os-org/ANOLISA — https://github.com/agentic-os-org/ANOLISA
• haoruilee/awesome-agent-native-services — https://github.com/haoruilee/awesome-agent-native-services
• lobu-ai/lobu — https://github.com/lobu-ai/lobu