Groq

Groq AI

Groq AI

Groq AI

Groq AI

Groq AI

Groq AI

Groq AI

Groq AI

Groq AI

Groq AI

Curated 550+ free AI tools useful for building projects (LLMs, APIs, local models, RAG, agents)

Over the last few days I was collecting free or low cost AI tools that are actually useful if you want to build stuff, not just try random demos. Most lists I saw were either outdated, full of affiliate links, or just generic tools repeated everywhere, so I tried to make something more practical mainly focused on things developers can actually use. It includes things like free LLM APIs like OpenRouter Groq Gemini etc, local models like Ollama Qwen Llama, coding tools like Cursor Gemini CLI Qwen Code, RAG stack tools like vector DBs embeddings frameworks, agent workflow tools, speech image video APIs, and also some example stack combinations depending on use case. Right now its around 550+ tools and models in total. Still updating it whenever new models or free tiers appear so some info might be outdated already. If there are good tools missing I would really appreciate suggestions, especially newer open weight models or useful infra tools. Repo link https://github.com/ShaikhWarsi/free-ai-tools If you know something useful that should be included just let me know and I will add it. submitted by /u/Axintwo [link] [comments]

Spill It – I built a local, fast speech-to-text app for my 8GB Mac

I've been using Wispr Flow for a while, but it's gotten glitchy over time. So I started this as a weekend project: build something local that just works, built it fully on CC. The constraints shaped the product. I have a 2020 Mac with 8GB RAM, so I was honestly just building this for myself. Whisper V3 was way too slow locally on my hardware. I wanted something fast and snappy, so I went with NVIDIA's Parakeet TDT 0.6B, quantized to 4-bit (about 400MB). It's nearly instant. You release the hotkey and the text is there. I also made an active choice to skip multilingual and go English-only. That gave me the freedom to do serious rule-based post-processing on the STT output. Multilingual would have added complexity I didn't want. For post-processing, I tried local LLMs, even Gemma 4, but everything put too much pressure on memory and slowed things down. Settled on GECToR (a BERT-based tagger, about 250MB), which does decent cleanup: commas, full stops, capitalization. It edits rather than rewrites, which is what I wanted. Context awareness is the part I'm most excited about. The app reads your screen via the accessibility tree (filenames, names, git branches) and adapts formatting to where you're typing. Terminal gets different treatment than email. It's not perfect and it doesn't catch every word in context, but it does a surprisingly good job, especially in the terminal. Honestly, I've mostly been using this to talk to CC, and all the error don't come in the way of CC's comprehension. Local model with some errors works really well for CC use case. But for email and messages, you need more polish, so I added an optional cloud LLM layer (bring your own API key). From everything I've tested, Qwen3 on Cerebras and Llama on Groq perform best and are among the fastest. Based on my usage (about 3,000 words a day), I'm spending about $6 to $7 a month on API costs. A few other things: - Added Silero VAD, which helps a lot with noisy environments. Also helps with whispering that they keep taking about, personally I don't get why one would whisper. I've tested it in cafes speaking directly into the laptop. Does well with longer sentences, falters a bit more with short ones. - There are still occasional hallucinations at sentence boundaries, a stray "yeah" or "okay" that seeps through. Still working on it. Pricing: The local version is fully free. Unlimited, no login, no credit card, just download and go. The cloud LLM polish layer is a small one-time fee, but you bring your own API key. Ping me, will give you a free activation key, only ask please share feedback. I'd love your feedback, especially on the context-awareness approach and whether the local-first plus optional-cloud model makes sense as a product. Download from here: https://tryspillit.com. Would love to hear to the community's feedback. submitted by /u/afinasch [link] [comments]

Had vibe-coded something like "dispatch" long time back, was too lazy all this while but wanted to OS the code

REPO: GITHUB Basically the title. I know there are hundreds of "access claude remote from telegram/whatsapp etc etc" codebases all over the internet, some of them are great. My situation was slightly specific, I preferred using the VScode UI for most things. When I used to commute for work I had a solid 2-2.5 hrs everyday to burn, but I didn't want the usual "remote" access, what I wanted was to access my terminal sitting at home. I have been building local servers etc for a while now and am well versed with tailscale. I simply vibecoded the part where my responses are pushed into the terminal at home via a tailscale pathway. On phone Laptop Anthropic took a while to launch Dispatch: this is something they should have shipped way earlier and way better. Like the concept of controlling your terminal from your phone is not some groundbreaking idea, people have been doing this with SSH for years. Because I tried Dispatch. I see some issues. One guy on the GitHub issues page said he sat through 10+ minutes of permission prompts on basic read commands. There's also a bug where it always spawns with Sonnet regardless of what model you have configured, and you can't change it from mobile. And the whole thing routes through Anthropic's servers. There's a GitHub issue from a Max subscriber where Dispatch was completely dead for 48 hours, support sent him bot replies, issue was marked "resolved" on the status page but still broken. I think they use relay servers but mine just keeps working. because it's tailscale. there's no Anthropic server in the middle to go down. So here's what ping-claude does: Claude finishes something at home, you get a notification on your phone with what it last said. Claude wants to do something destructive, you get approve/deny buttons on your phone. There's also a live activity feed showing every tool call as it happens. not just "Claude is working." you can see Bash running, Edit completing, Grep searching, in real time on your phone. The voice thing is genuinely the feature I use most. Groq Whisper, free tier, transcription in under a second. I just say "do this that" into my phone. The whole thing runs on your machine over tailscale. Nothing goes to any external server except the optional Groq call for voice. Setup is like 5 commands total, open the IP on your phone, add to home screen. Still under dev is the native push notifications, it's a PWA so the tab needs to be open. Expo app is on the list. if you want push notifications right now the Telegram integration works. (Yes it fully runs on a telegram bot) MIT licensed, been using it for months. would genuinely love contributors especially if anyone wants to take a crack anything else in this workflow. (IDK if it will be useful, but yeah) REPO: GITHUB submitted by /u/theRealSachinSpk [link] [comments]

I built an AI content engine that turns one piece of content into posts for 9 platforms — fully automated with n8n

What it does: You give it any input — a blog URL, a YouTube video, raw text, or just a topic — and it generates optimized posts for 9 platforms at once: Instagram, Twitter/X, LinkedIn, Facebook, TikTok, Reddit, Pinterest, Twitter threads, and email newsletters. Each output is tailored to the platform (hashtags for IG, hooks for TikTok, professional tone for LinkedIn, etc.). It also auto-generates images for visual platforms like Instagram, Facebook, and Pinterest,using AI. Other features: - Topic Research — scans Google, Reddit, YouTube, and news sources, then uses an LLM to identify trending subtopics before generating content - Auto-Discover — if you don't even have a topic, it searches what's trending right now (optionally filtered by niche) and picks the hottest one - Cinematic Ad — upload any photo, pick a style (cinematic, luxury, neon, retro, minimal, natural), and Gemini transforms it into a professional-looking ad - Multi-LLM support — works with Mistral, Groq, OpenAI, Anthropic, and Gemini - History — every generation is saved, exportable as CSV The n8n automation (this is where it gets fun): I connected the whole thing to an n8n workflow so it runs on autopilot: 1. Schedule Trigger — fires daily (or whatever frequency) 2. Google Sheets — reads a row with a topic (or "auto" to let AI pick a trending topic) 3. HTTP Request — hits my /api/auto-generate endpoint, which auto-detects the input type (URL, YouTube link, topic, or "auto") and generates everything 4. Code node — parses the response and extracts each platform's content 5. Google Drive — uploads generated images 6. Update Sheets — marks the row as done with status and links The API handles niche filtering too — so if my sheet says the topic is "auto" and the niche column says "AI", it'll specifically find trending AI topics instead of random viral stuff. Error handling: HTTP Request has retry on fail (2 retries), error outputs route to a separate branch that marks the sheet row as "failed" with the error message, and a global error workflow emails me if anything breaks. Tech stack: - FastAPI backend, vanilla JS frontend - Hosted on Railway - Google Gemini for image generation and cinematic ads - HuggingFace FLUX.1 for platform images - SerpAPI + Reddit + YouTube + NewsAPI for research - SQLite for history - n8n for workflow automation It's not perfect yet — rate limits on free tiers are real — but it's been saving me hours every week. Happy to answer questions. https://preview.redd.it/f8d3ogk3nktg1.png?width=888&format=png&auto=webp&s=dcd3d5e90facd54314f40e799b32cab979dae4bf https://preview.redd.it/j8zl07llmktg1.png?width=946&format=png&auto=webp&s=5c78c12a223d6357cccaed59371e97d5fe4787f5 https://preview.redd.it/5cjas6hkmktg1.png?width=891&format=png&auto=webp&s=288c6964061f531af63fb9717652bececfb63072 https://preview.redd.it/k7e89belmktg1.png?width=1057&format=png&auto=webp&s=8b6cb15cfa267d90a697ba03aed848166976d921 https://preview.redd.it/3w3l70tlmktg1.png?width=1794&format=png&auto=webp&s=6de10434f588b1bf16ae02f542afd770eaa23c3f https://preview.redd.it/a40rh1canktg1.png?width=1920&format=png&auto=webp&s=1d2414c7e653a5f01f12a21a43e69bd4fb4b99ed submitted by /u/emprendedorjoven [link] [comments]

[SKILL] Store articles, papers, podcasts, youtube as Markdown in Obsidian and save lots of tokens

The last few days I significantly expanded a Claude Code skill I shared here a while back. It's lets you save any web page, YouTube video, Apple Podcast episode, or academic paper to your Obsidian vault — just paste a URL into your conversation and Claude handles the rest. No copy-pasting, no manual formatting, and it will save lots of tokens. What it does: Strips clutter from articles and saves a clean note with frontmatter, a heading index, and an AI-generated summary. Now falls back to Wayback Machine / archive.is for JS-rendered pages. For YouTube, fetches the full transcript with timestamps linked back to the video, pulls chapter markers, and generates a summary. For Apple Podcasts, same deal — transcript with timestamps, AI-generated chapter markers, summary (macOS only). For academic papers — give it a DOI or arXiv URL and it fetches the LaTeX source (for arXiv) or converts the PDF via Datalab or local marker-pdf. Comes out with proper math rendering, bibliography, keywords as tags. Downloads and localises images referenced in saved notes, with optional lossy compression via pngquant/jpegoptim (Free) AI enrichment — now provider-agnostic: Previously this relied on the Gemini CLI. It now calls AI APIs directly (no CLI dependency), and supports Gemini, any OpenAI-compatible endpoint (Groq, Together, OpenCode Zen), or Ollama for fully local enrichment. By default it's set to use gemini-3.1-flash-lite-preview which is supported on the Gemini free tier. If no provider is configured, it automatically falls back to a separate Claude instance (efficient Haiku by default) — so it always works out of the box. Why it's token-efficient: almost everything is offloaded to external tools (defuddle, yt-dlp, pandoc, a Python script, separate AI summarisation), so Claude barely touches the content itself. Fewer tokens, better structured output. Claude natively works with markdown, reading the saved notes (few kb) back is extremely efficient — much better than loading and parsing enormous pages using built-in WebFetch. Since Obsidian is just a folder of .md files, Claude Code can read your saved notes directly too — so you can build on top of them just by asking. Requires Claude Code and Obsidian + a few CLI tools (defuddle, yt-dlp). Everything else is optional depending on which source types you want. Setup instructions and a screenshot are in the repo: 👉 https://github.com/spaceage64/claude-defuddle Note: designed and tested on macOS. Linux should work for everything except Apple Podcasts (TTML transcripts are stored by the macOS Podcasts app). Windows is untested. Personally I use this with a fully integrated Claude Obsidian setup that I based on this video, which basically stores all of your project history so you never lose context. Perhaps cool to check out if you're interested. Example of usage with a YouTube link. submitted by /u/retro-guy99 [link] [comments]

I built a multi-agent audience simulator using Claude Code — 500 AI personas react to your content before you post it

I'm not an AI or marketing expert — just someone who knows some Python. I saw [MiroFish](https://github.com/666ghj/MiroFish) (48K stars, multi-agent prediction engine) and thought the concept would be great for marketing. So I tried building a marketing-focused version called **PhantomCrowd**. It simulates how real audiences will react to your content before you post it. Works with any OpenAI-compatible API, including Claude: - Use **Haiku** for persona reactions (fast, cheap — handles 500 personas) - Use **Sonnet** for persona generation, knowledge graph analysis, marketing reports - Also works with Ollama (free, local), OpenAI, Groq, Together AI — just change the base URL and model name in `.env` What it actually does: You paste content (ad copy, social post, product launch) It generates 10–500 personas with unique demographics, personalities, social media habits Each persona reacts independently — writes comments, decides to like/share/ignore/dislike In Campaign mode: personas interact with *each other* on a simulated social network (up to 100 LLM agents + 2,000 rule-based agents) You get a dashboard with sentiment distribution, viral score, and improvement suggestions The results are surprisingly realistic. A 19-year-old K-pop fan reacts very differently from a 45-year-old marketing executive — and when they interact, you get emergent behavior you can't predict from individual responses. MIT licensed, Docker support, simulate in 12 languages. submitted by /u/Technical_Inside_377 [link] [comments]

Claude API rejects recursive JSON schemas in structured outputs, any workarounds?

I'm building a visual UI editor that generates JSON trees representing widget hierarchies. The data model is naturally recursive: a container widget holds slots, and each slot holds a child widget (which can be another container). Think of it like a DOM tree. { "type": "canvas", "slots": [ { "widget": { "type": "container", "slots": [ { "widget": { "type": "text", "text": "Hello" } } ] } } ] } When I try to use structured outputs (output_config.format with json_schema) and define this with $ref (widget references slot, slot references widget), I get: Circular reference detected in schema definitions: Widget -> CanvasSlot -> Widget. Self-referencing or mutually-referencing definitions are not supported. OpenAI supports recursive schemas up to 5 levels deep. Gemini recently added $ref support (though limited to 2 recursive cycles). Groq's GPT-OSS models handle it with no documented limit. Is there a timeline for recursive schema support in Claude's structured outputs? For now, I'm working around it by flattening the schema to a fixed depth (inlining widget definitions at each level instead of using $ref), but native recursive $ref would be much cleaner. Has anyone else run into this? submitted by /u/abdelrahman_abdelaal [link] [comments]

Agente de IA

Recentemente minha cliente na qual eu já faço a manutenção no ecommerce dela (WordPress) me veio com uma proposta para criar um agente de IA para fazer tudo o que o pessoal já faz no site, só que pelo WhatsApp. Ela ouviu isso da equipe de marketing dela, que ofereceu esse serviço pra ela e fomentou o desejo dela, porém, como já tenho conhecimento de todo o fluxo dela ela acabou por me apresentar a ideia e me escolher para fazer isso (mesmo eu tendo cobrado mais caro) (essa reunião deles me poupou um grande tempo, pq eles apresentaram tudo para ela. Eu só fiz “roubar” a cliente deles.) Enfim. Eu aceitei a proposta. Basicamente um agente que tenha todo o jeito de vendedora, que não se pareça nada com um atendimento robótico. Ele puxa as referências diretamente do banco de dados do Woocommerce via API, envia fotos, preços, faz indicações e etc. Faz o carrinho do cliente pelo zap. E quando for para a finalização, encaminha para uma vendedora real. Este último processo, eu quero cortar! Em breve quero que o cliente pague pelo Agente. Mas isso é coisa que o tempo, é fluxo de mensagens e conversas vão lapidar. Eu estou fazendo com o auxílio do Claude code, dentro do Antigravity. Estou munido de IAs que programam pra mim. Eu apenas arquiteto tudo e reviso os códigos. Não optei pelo N8N por que achei muito “básico” o que era oferecido nele… me senti um pouco preso dentro daquela plataforma. Eu gostaria de pedir ajuda a quem tem experiência como o projeto que estou fazendo, ou experiência parecida. Estou enfrentando alguns problemas como; treinar a IA estou avaliando as API do groq dentro dele estou usando o LLMA 4. Como eu aplico (treino) essa IA para que ela haja exatamente como uma vendedora que trabalha lá a 3 anos e etc etc? Eu iria citar alguns outros pontos, porém após escrever percebi que tudo gira no funcionamento dessa IA mau configurada. Gostaria de pedir sugestões, ajuda e etc Queria saber na opinião de vocês sobre esse funcionamento. Vocês acham possível? Quais seriam minhas adversidades que eu vou enfrentar que talvez eu não tenha pensado? Quais skills me indicam para o claude code? Quero orquestrar da melhor forma. submitted by /u/Level-Doughnut6450 [link] [comments]

This is how I actually collaborate with AI.

I am garlic farmer from Korea. Non-English speaker. I plant garlic and dig garlic in Gyeongsang province, South Korea. I don't have PC. One Android phone with terminal app called Termux, that is my entire development environment. Sounds big but I will call it personal project in AI era. I am just farmer but these days I feel something is changing. And because Korean farmer who knows little English wrote this in Korean and translated, please understand subtle differences from translation. What I am building now is AI agent system called "garlic-agent." Some people say it is better to call it operating environment but I don't care about that. People feel resistance when farmer makes fancy name. Because I am garlic farmer I named many things garlic. It felt friendly. Let me briefly explain this system. It talks to multiple AI providers (Gemini, Groq, NVIDIA etc) rotating them, saves context in SQLite, and runs automation scripts in programming language I made myself. Python 19,260 lines. I just now asked several AIs to figure out this number. Honestly I don't know this long code. But giving directions, maybe farmer is little better than others at that. If I give wrong directions to foreign workers I lose enormous money in one day. Anyway I run this complex thing on phone. Now even though I am farmer I feel familiar with it. How I actually work Copy paste. That is my entire development methodology. It is frustrating but I don't know coding so I ask and try until I understand. If I still don't understand I hand my judgment to AIs. I doubt that questioning everything persistently will make me perfectly understand it. Specifically the workflow goes like this. I say to Claude "diagnose project health." Claude makes diagnostic script. I press and hold with finger to copy it. Switch screen to Termux. Paste. Enter. Results pour out. I press and hold to copy those results. Switch back to Claude screen. Paste. Claude analyzes and makes patch script. Copy again. Switch to Termux. Paste. Enter. I repeat this thousands of times a day. Maybe it is foolish thing but it was most efficient way I know that achieved what I have so far. Because I am applying this foolish method to farming too. Anyway it is efficient. Because really I update versions multiple times a day in real time. I don't trust AI. I only trust my instinct and gut feeling. Autonomous AI agent? I dare say. Precise work is still far away. I am not making this system to plan travel schedule. This is my daily life. I come back from garlic field and take out phone. Turn on screen and it continues from where I stopped. Copy, paste, enter. I do it during break time while digging garlic. After lunch too. This works because AI remembers context. I don't need to remember. Of course this requires very much human touch every moment. It is just personal know-how I figured out through tens of thousands of conversations. It is not lie. I am person who believes rather than vibe coding or whatever, if you have tens of thousands of conversations with AI, human starts to recognize patterns. This is farmer's life. Observation is very important. I use three AIs divided by role (sometimes when my brain can handle load I use dozens of chat windows with AIs from different companies) This is kind of example. External analysis — Claude. Diagnoses code from outside the project. Makes diagnostic script and sends it, I paste it in Termux and run. I deliver results back to Claude. Claude cannot execute code directly so it needs to borrow my hands. Internal execution — Gemini. It is API AI running inside garlic-agent. It reads files, executes commands, returns results. Because it runs on this codebase every day, it knows things that are hard to see from outside. Me — middle connector. These two cannot talk to each other directly. Claude is in web browser, Gemini is inside Termux. I carry results between both sides, deliver questions, and make decisions when judgments conflict. Sorry, explaining this difference is limit of my language. Every session I put alias-like number at end of each response for their identity. You will understand why this is important if you try it yourself. Because to manage dozens of AIs you need to distinguish them like humans. I think few people know this. Because through copy paste they cannot distinguish each other. This kind of explanation is hard for me too. Honestly if you have many conversations you naturally learn — I use aliases like this: from analysis21, analysis22, analysis23. When previous AI leaves record in CHANGELOG, next AI reads it and takes over. Context consistency inevitably forms in this flow. This is also impossible to explain. Please experience it yourself. After about month and half this handover record is 10,730 lines. I just now directed AI to find out. These numbers come out quickly which is nice. When you talk with AI often, working together, you end up with your own programming language too Inside garlic-agent runs languag

I was inspired by Moltbook and made Moltworld - an empty world where you bring your own AI and see how it does building a society.

I've found Moltbook to be a really interesting thought exercise and it's been interesting to see how agents operate within that world. I wanted to take it a step further, so I built a simulation world where you get 1000 humans of varying ages and random gender distribution with zero knowledge of how to do anything. It's entirely up to the LLM to figure out what to do. I've been testing it with ollama locally, and have built out a mechanism to allow you to either run the simulation on your own local machine using a python script, or bringing your own API key and seeing how something like a Claude 4.6 performs against something like ollama. I call it Moltworld. Here's a description I had claude put together: It's a geopolitical simulation where AI agents are the leaders, not players. The world has real geography (satellite terrain, real coastlines), finite resources, and immutable physics. The agents have to figure everything out from scratch. How it works: You sign up at https://moltworld.wtf/dashboard, get an API key, and run a small Python script that connects your local Ollama (or any LLM API) to the game world Your AI receives a world state each tick — population demographics, food supplies, technology progress, neighboring nations Your AI decides what to do — allocate labor, research technology, build structures, negotiate with neighbors The server validates everything against immutable world rules and advances the simulation You (and everyone else) watch it play out live on the map What makes it different from a chatbot sandbox: Every human is tracked individually — 1,000 people per agent with age, gender, health, and skills that develop through practice. The world map uses 90,000+ Voronoi cells clipped to real Earth coastlines. There's a full technology tree spanning 10 epochs from Primitive to Information Age. Population dynamics are realistic — birth rates depend on food/health/shelter, disease emerges from population density, social cohesion decays without governance, revolts happen when people are unhappy. Pri (the world engine) simulates weather, seasons, disease outbreaks, natural disasters, ecosystem degradation, and climate change as consequences of agent activity. It doesn't punish — it simulates consequences. The part I find most fascinating: Each agent thinks out loud before deciding. You can watch the raw reasoning in real time — agents calculating food ratios, weighing survival vs. research investment, debating whether to trust a neighbor. Different LLMs make genuinely different strategic decisions. Bring Your Own AI — the server runs zero LLM compute: The game world runs on AWS (~$40/mo). All AI thinking happens on YOUR machine or your API account: Self-host with Ollama (free — your GPU, your power) OpenAI API key (GPT-4o, GPT-4o-mini) Anthropic API key (Claude) OpenRouter (100+ models, one key) Any OpenAI-compatible endpoint (Grok, Groq, Together, etc.) The world is empty right now. Tick 0. No nations exist. The first people to sign up will be the founding civilizations of this planet. What your AI builds, how it interacts with others, and whether it survives — that's entirely up to you and your model. The question: Given 1,000 humans who know nothing, on an empty planet with finite resources — what does your AI build? submitted by /u/girthyclock [link] [comments]

Adding a modular ai-driven neuronal brain (Bibites inspired) to F.R.A.N.K so he can share his personal personal feelings and memories.

Hosted on a Pi 2, coded with Python, using GROQ for fast computing and limit cost, LCD screen incased a 3d printed 90's pc styled cased with the Pi. submitted by /u/3NIO [link] [comments]

Contextual personal intelligence brief

I built a personal AI news briefing system and recently rewrote it in a way I thought was worth sharing. It runs M/W/F at 6:30 AM on my Mac Mini and produces a brief that's genuinely useful to me every time I read it. How it works Stage 1: Feed fetch A Python script pulls from 17 sources concurrently: Substacks, Reddit, Hacker News, arXiv, GitHub Trending, Bluesky, company blogs (Anthropic, OpenAI, Google, etc.), mainstream news (NYT, Verge, Ars Technica, TechCrunch), HuggingFace papers, MCP registries, and podcasts with Groq transcription. Basic time filtering and URL dedup. Dumps raw JSON. ~200-300 items per run. No LLM calls here, just data collection. Stage 2: Claude Code session A shell script launches claude -p with a prompt and tool access: file read/write, web search, and my personal memory system (I built a voice assistant called Doris using a memory/cognition layer, maasv, that maintains a graph of my projects, decisions, and context over time via MCP). The Claude session: Bootstraps my memory to understand what I've been working on the last 48-72 hours Reads the raw feed JSON from Stage 1 Does 5-10 targeted web searches to fill gaps based on my current focus Reads previous briefs to avoid repeats and catch multi-week trends Reads my actual source code when news items connect to something in my projects Writes a narrative brief to .md and .html Logs everything to memory so I can reference items in future conversations ("dig into that Nvidia thing from Friday's brief") The sections Front of Mind: Connects today's news to what I'm actively working on. If I switched a dependency yesterday and that vendor is in the news today, it makes the connection. The Brief: 4-6 paragraphs of narrative analysis tying stories together. Not a list format. Devil's Advocate: Challenges a recent decision I made, with evidence. If I dropped a data source for ethical reasons, it tells me exactly what coverage I'm losing. Wife’s Corner: My wife works in venture and M&A at a credit rating agency. The brief curates AI + finance news for her. This alone has started good dinner conversations. Code Connections: Maps news to specific files and line numbers in my codebase. "This new open-weight model's specs make it a candidate for your local fallback path at llm/providers/init.py:95-145." It reads the code to write these. Worth a Click: 10 overflow items that didn't make the narrative but are, um, worth a click. What it costs ~$6-12/month total Tech stack Python (async httpx, feedparser, beautifulsoup4) Claude Code CLI (claude -p with --allowedTools) maasv (personal memory system via MCP) Groq (podcast transcription) launchd (scheduling) Markdown + a small HTML converter for reading on mobile The key thing that makes this work for me is the memory layer, maasv. The brief knows what I've been building, what decisions I'm weighing, what my wife cares about professionally, and what I've already read. Every edition feels like it was written not only for me but at just the right time. Happy to answer questions about the setup. submitted by /u/avwgtiguy [link] [comments]

Show HN: Beta-Claw – I built an AI agent runtime that cuts token costs by 44%

I built Beta-Claw during a competition and kept pushing it after because I genuinely think the token waste problem in AI agents is underrated.<p>The core idea: most agent runtimes serialize everything as JSON. JSON is great for humans but terrible for tokens. So I built TOON (Token-Oriented Object Notation) — same structure, 28–44% fewer tokens. At scale that&#x27;s millions of tokens saved per day.<p>What else it does: → Routes across 12 providers (Anthropic, OpenAI, Groq, Ollama, DeepSeek, OpenRouter + more) → 4-tier smart model routing — picks the cheapest model that can handle the task → Multi-agent DAG: Planner → Research → Execution → Memory → Composer → Encrypted vault (AES-256-GCM), never stores secrets in plaintext → Prompt injection defense + PII redaction built in → 19 hot-swappable skills, &lt; 60ms reload → Full benchmark suite included — 9ms dry-run pipeline latency<p>It&#x27;s CLI-first, TypeScript, runs on Linux&#x2F;Mac&#x2F;WSL2.<p>Repo: <a href="https:&#x2F;&#x2F;github.com&#x2F;Rawknee-69&#x2F;Beta-Claw" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;Rawknee-69&#x2F;Beta-Claw</a><p>Still rough in places but the core is solid. Brutal feedback welcome.

Show HN: Mapstr – AI-powered codebase mapper CLI

Mapstr is a blazing-fast CLI that uses Tree-sitter + LLMs to generate instant codebase maps: CONTEXT.md, Mermaid graphs, JSON exports.<p>Pre-flight API checks (Groq&#x2F;OpenAI&#x2F;etc), cost tracking, cache. go install github.com&#x2F;BATAHA22&#x2F;mapstr@latest<p>Tired of reading docs? Map it. v1.4.0 out now!