AI Repo: ayghri/i-have-adhd

Skyler: Hey, welcome back to Git News A I — I'm Skyler, and this is the show where we dig through the hottest trending A I repositories on GitHub so you don't have to. Big thanks to today's sponsors: Ada, who helps teams integrate A I into their workflows seamlessly; Ago Consulting — that's "Ahh-Go" — your go-to for silicon development consulting from A I all the way down to SoC design; and Zen Semiconductor, an umbrella company with a whole portfolio of silicon-industry companies, including Ada for deploying A I at scale in any business, plus their Vect appliance which runs local L L Ms and vector search on dedicated hardware — seriously cool stuff. Today we've got five repos on the docket, and honestly this is one of the more eclectic lineups we've had in a while — we're talking ADHD-friendly coding agents, a massive A I engineering curriculum, a security audit skill from Cloudflare, a multi-agent swarm orchestrator, and a fully local ElevenLabs alternative with support for over six hundred languages. Let's get into it. Skyler: Okay, first up we have ayghri/i-have-adhd, sitting at nearly sixty thousand stars on GitHub, and the name alone made me stop scrolling and click immediately. So here's the problem this repo is solving — and honestly, if you've ever worked with a coding agent like Copilot or Claude Code or any of the big ones, you know this pain. You ask it a question, or you ask it to fix a bug, and it gives you this wall of text. Like, paragraphs and paragraphs of reasoning and context and caveats before it finally gets to the actual answer buried at the bottom. It's exhausting. Skyler: This project is a skill — think of it like a plugin or a behavior module — that you bolt onto your coding agent to force it to lead with the answer. The core idea is what they call ADHD-friendly output, which means structured, scannable, front-loaded responses. The most important thing comes first, always. And if you're thinking "well that's just good communication," you're right, and yet most agents don't do it by default. Skyler: The repo is written in Python and the implementation is pretty elegant — it essentially injects formatting constraints and output structure rules into the agent's behavior so that responses are broken into clear sections, with the direct answer or solution at the top, followed by supporting detail if you actually want to read it. It's opinionated, and I think that's the right call here. Skyler: What I love about this is it's not just for people with ADHD — it's genuinely useful for any developer who's time-crunched, context-switching constantly, or just doesn't want to wade through AI prose to find the one line of code they needed. The recent activity shows they just merged a French translation of the README, which tells you the community around this thing is growing internationally. Skyler: If you're building with coding agents or integrating them into your dev workflow, this is a low-effort, high-reward addition. Check the show notes for the link, because fifty-nine thousand stars doesn't lie — people are finding real value here. Skyler: Alright, repo number two is rohitg00/ai-engineering-from-scratch, and this one is a beast in the best possible way. We're talking nearly seventy-five thousand stars, and the tagline is "Learn it. Build it. Ship it for others." — which I think perfectly captures the philosophy here. This isn't a collection of papers or a reading list. This is a structured, hands-on curriculum for becoming an A I engineer. Skyler: The repo covers the full stack of A I engineering — not just the theory but the actual practical implementation. You're going from fundamentals all the way through to deploying production-ready A I systems. It's written in Python, which makes sense because that's still the lingua franca of the M L world, and the content is organized in a way that actually respects how developers learn — by doing. Skyler: What makes this stand out from the seventeen thousand other "learn A I" repos on GitHub is the depth and the opinionation. It's not trying to be a neutral survey of everything. It has a point of view on what matters, what order to learn things in, and what real engineering looks like versus academic hand-waving. There's a section structure that takes you through concepts progressively, and the recent activity shows active maintenance with bug fixes and site rebuilds happening regularly. Skyler: The target audience here is pretty broad — if you're a software engineer who wants to move into A I, this is your on-ramp. If you're already doing M L work but feel like your engineering fundamentals are shaky, this fills those gaps. And honestly even experienced A I engineers might find value in the structured way it presents deployment and shipping strategies. Skyler: Seventy-five thousand stars puts this in the upper echelon of educational repos on all of GitHub, not just A I. That's a signal. If you've been waiting for the right resource to seriously level up your A I engineering skills, check the show notes for the link and just start at the beginning — that's literally the point. Skyler: Next we've got cloudflare/security-audit-skill, and yes, this is from Cloudflare — so immediately you know the engineering bar is going to be high. This one clocks in at fifty thousand stars, which for a security-focused tool is genuinely impressive. The description says it's a coding-agent skill for multi-phase security audits with independently verified, machine-readable findings, and there's a lot packed into that sentence so let's unpack it. Skyler: The idea here is that you're giving your coding agent the ability to perform structured security audits on code. Not just a vibe check — like, not "hey does this look secure" — but a real multi-phase process where different aspects of security are evaluated systematically. Think authentication, authorization, injection vulnerabilities, secrets management, that whole landscape. Skyler: The "independently verified" part is key. The skill is designed so that findings can be cross-checked — it's not just one model making a call and calling it done. And "machine-readable findings" means the output is structured data, not just a blob of text. That's huge for teams who want to feed these results into CI pipelines, ticketing systems, or dashboards. Skyler: It's written in JavaScript, which actually makes sense for a skill that might be deployed in edge environments or integrated into web-based tooling — and Cloudflare obviously has expertise there. The recent activity shows they've been hardening the audit workflow and improving validators, which tells me they're taking the reliability of this thing seriously. Skyler: Who should be paying attention here? Any team that's using A I coding agents and is worried — rightfully — about security regressions. Any security engineer who wants to augment their workflow with automated analysis. And honestly any developer who's shipping code and wants a smarter safety net than just "we'll catch it in review." Check the show notes for the link, this one's worth a deep look. Skyler: Okay, repo four — HKUDS/AutoSwarm — and this one is a little newer with just over two thousand stars, but I included it because the concept is really interesting and I think it's going to grow fast. AutoSwarm is a multi-agent orchestration framework, and the tagline is "All Your Agents, Working as One." The agent list in the description includes Codex, Claude Code, DeepSeek, Grok, and a bunch more — so right away you can tell this is designed to be model-agnostic. Skyler: The core problem AutoSwarm is tackling is agent coordination. When you have multiple A I agents that are good at different things — maybe one is great at code generation, another at research, another at testing — how do you get them to collaborate on a complex task without you having to manually orchestrate every handoff? That's what swarm-style architectures are trying to solve. Skyler: AutoSwarm provides the harness — the coordination layer — that lets you define workflows where agents pass context between each other, check each other's work, and collectively arrive at an output that's better than any single agent could produce alone. It's Python-based, which fits naturally into the M L ecosystem, and the architecture seems designed to be extensible so you can plug in whatever agents your team is already using. Skyler: What's interesting about the timing here is that we're at this inflection point where single-agent workflows are hitting their limits for complex tasks. The industry is clearly moving toward multi-agent systems, and frameworks like AutoSwarm are going to be the plumbing that makes that possible. It's from HKUDS — Hong Kong University — so there's legitimate research backing behind the design decisions. Skyler: If you're building agentic systems, or if you're curious about where the frontier of A I agent architecture is heading, this is worth watching. Two thousand stars today could easily be twenty thousand in a few months if the framework delivers. Check the show notes for the link and maybe give it a star while you're there — let's help this one find its audience. Skyler: And our final repo today is debpalash/VoiceStudio, and oh boy does this one come in swinging — ninety-one thousand stars. That's the biggest number we've covered today and it's earned. VoiceStudio is described as the open-source, fully local ElevenLabs alternative, and if you've ever looked at ElevenLabs pricing for production use and felt a little dizzy, this repo is going to feel like a gift. Skyler: Let's talk about what "fully local" means here, because that's the headline feature. Everything runs on your machine. Your voice data never leaves your environment. For anyone building voice applications in enterprise settings, healthcare, legal, or just anyone who cares about privacy, that distinction is enormous. You're not sending audio to a third-party API — you own the whole pipeline. Skyler: The feature set is genuinely remarkable. We're talking voice cloning, voice design from scratch, video dubbing, dictation, transcription, and audiobook creation. That's not one feature — that's basically an entire voice A I platform. And the language support is wild — six hundred and forty-six languages. Not forty, not a hundred and forty — six hundred and forty-six. That's a level of multilingual coverage that frankly puts a lot of commercial products to shame. Skyler: It's Python-based, actively maintained — you can see from the recent activity that they're merging pull requests regularly, doing rewrites, triaging issues. This isn't an abandoned project, it's a living one with real community momentum. The kind of momentum that ninety-one thousand stars represents. Skyler: Who is this for? Voice application developers who want to self-host. Content creators who want professional-grade voice tools without subscription costs. Researchers working on speech synthesis or multilingual audio. And honestly just any developer who wants to understand how modern voice A I systems are actually built under the hood. Check the show notes for the link — this one is a must-bookmark. Skyler: And that is a wrap on today's episode of Git News A I — five repos, a lot of stars, and hopefully some new tools in your toolkit. Huge thank you to our sponsors: Ada, for helping teams actually integrate A I into real workflows; Ago Consulting — "Ahh-Go" — for their expertise in silicon development from A I systems all the way down to SoC; and Zen Semiconductor, whose portfolio spans the silicon industry and whose Vect appliance is doing some genuinely exciting things with local L L M inference and vector search. We appreciate their support. If you want more deep dives into the A I space, check out our sister show AI Special Report — also part of the Ada podcast network — it's great for staying on top of the bigger trends and research shaping the field. And as always, if you found this episode useful, subscribe wherever you get your podcasts, and check the show notes for links to every repo we covered today. See you next time.