We now support rich formatting for all chatbots. Tables, nested lists, inline media, formulas, headers and more — right in Telegram messages. 🔨 Start building! Docs: core.telegram.org/bots/api#r…

Today on the blog, we discuss a pathway for the second life of phones through the exploration of “phone cluster computing”, which can directly reduce the environmental footprint of computing by avoiding the need for further raw material extraction. More →goo.gle/4aJe5vO

Fable 5 was banned by the US government yesterday. It's time to build your own Personal AI Computer and run local models. So no one can ever cut you off. Here's how ↓

1 buy a computer with lots of RAM 2 download hermes and set it up with local models 3 create a gateway to talk to it privately from any device 4 use llm-wiki.net to build a wiki (or multiple wikis) with a local reasoning model 5 use gbrain on top of llm-wiki for the memory retrieval layer using local re-ranker way better UX than using ChatGPT or Claude apps.

Beautiful paper from Google DeepMind. Explains the pathways from AGI to ASI, and why that jump could happen through several routes. The authors frame the AGI-to-ASI transition around 4 technical pathways: - continued scaling of compute, model size, data, and test-time inference; - algorithmic paradigm shifts beyond today’s transformer-based foundation-model stack; - recursive self-improvement, where AI accelerates AI R&D and improves future systems; and - multi-agent collective intelligence, where large populations of specialized agents coordinate into a superhuman group agent. Scaling may work for a while, but it could hit limits in data, compute, energy, or weaker returns from making systems larger. Recursive improvement is the most uncertain path, because AI could speed up AI research, but that loop may also slow if hard research problems need real-world testing, scarce hardware, or new ideas. Multi-agent collectives may be the most underappreciated path, because a society of competent digital workers could outperform a brilliant individual model through specialization, speed, and coordination. The big point is that ASI may not arrive as 1 sudden event, but as a chain of faster changes as AI helps create better AI and stronger scientific tools. ---- Link – arxiv. org/abs/2606.12683 Title: "From AGI to ASI"

AI agent kamu nulis 80 baris code buat yang sebenarnya cuma butuh 1 baris? Kenalan sama Ponytail plugin yang bikin AI coding agent berpikir seperti laziest senior dev di dunia. "The best code is the code you never wrote." Cara kerjanya: 1. Cek dulu: Apakah ini perlu? (YAGNI) 2. Sudah ada di stdlib? 3. Ada native feature di platform? 4. Sudah ada di dependency? 5. Bisa 1 baris? 6. Baru nulis code minimal Hasil benchmark: • 80–94% less code • 47–77% cheaper • 3–6× faster Contoh: ❌ Agent nulis full date picker library wrapper logic ✅ Ponytail: <input type="date"> <!-- ponytail: browser has one --> Support: Hermes,Claude Code, Codex, Cursor, Antigravity, Pi Agent, OpenCode, dll. Repo: github.com/DietrichGebert/po… Pasang sekarang & rasain bedanya. Agent kamu bakal jauh lebih "senior" (dan males nulis code berlebihan) 😂 Kamu sering kesal AI agent over-engineering? Comment pengalamanmu 👇

AMD CEO LISA SU HELD A MINI PC ON STAGE THAT RUNS A 235B MODEL AND REPLACES YOUR $440/MONTH AI STACK amd's ryzen ai max 395 is the first x86 chip that runs a 200 billion parameter model on one piece of silicon. cpu and gpu share 128gb of unified memory, no separate graphics card needed the gmktec evo-x2 runs qwen3 235b fully, deepseek v3 comfortably and llama 3.3 70b with headroom. on linux you get 110gb of usable vram out of 128gb amd claimed the chip beat an nvidia rtx 5080 by more than 3x on deepseek r1 inference. a lunchbox sized pc outrunning a $1,000 discrete gpu on a real ai workload a heavy ai user pays $200 for claude code max, $200 for chatgpt pro, $20 for cursor and $20 for gemini. that's $5,280 a year and the box pays itself off in 9 to 10 months install ollama, pull the model, point claude code at localhost. same interface, nothing leaves the machine, nothing costs per request bookmark this and read the article below

Supertonic just killed ElevenLabs. A text-to-speech model that runs entirely on your device. No cloud. No API key. No per-character pricing. 2,700 GitHub stars. 100% open source. MIT licensed. The numbers are wild: → 167x faster than real-time on an M4 Pro → Only 66M parameters → 1,263 chars/sec vs ElevenLabs Flash at 287 → 1,048 chars/sec vs OpenAI TTS-1 at 55 → Runs on a Raspberry Pi. Runs on an e-reader in airplane mode. Reads currency, dates, phone numbers, and technical units correctly without preprocessing. ElevenLabs fails these. OpenAI fails these. Gemini fails these. Supports 11 platforms and 5 languages. Chrome extension turns any webpage into audio in under a second. I've watched on-device models lose to cloud APIs for years. This one doesn't lose. The cloud TTS business just got cooked.

What Europe should do right now: 1. Call all the European researchers working on AI and return them back with same salary (or they can stay but switch career). 2. Fill EU places having GPUs with money, and put those people there. 3. AI partnerships with China India.

DEJA DE PROMPTEAR. EMPIEZA A LOOPEAR. Encontré un sitio que recopila los loops más usados por la comunidad → loops.elorm.xyz Los propios creadores de Claude Code lo dicen: el futuro no es promptear, es diseñar loops. No sabes qué son ni cómo crearlos? Este articulo lo explica detalladamente👇

Karpathy said something you'll regret ignoring: "Remove yourself as the bottleneck. Maximize your leverage. Put in very few tokens, and a huge amount of stuff happens on your behalf." Loop engineering is the exact thing that does that. In a hand-run session, the operator handles two things: - deciding what the agent runs next - and checking its output before the next step Both are manual, and both decide how far the agent gets on its own without the operator. Loop engineering moves both steps into the system. A core operating structure surrounds the loop, and the diagram below depicts it. - A schedule decides what to run - Loop is the maker that produces the work - A separate checker agent grades the output - A file on disk holds the state they both read. The loop runs until either done, max iterations, or an exhausted budget. Here are some practical engineering considerations: 1) A model grading its own output justifies what it already did instead of catching where it failed. That's why a separate checker's findings return to the maker as the next instruction. And the cycle repeats until the checker finds nothing left to fix. 2) A loop with no stop condition burns tokens, and the cost climbs fast once sub-agents and long runs add up. That's why the exit must be set before the loop runs, not while it is running. A simple exit could be: ↳ fix only the major issues, run one final pass, and stop after two loops, with "all tests pass and lint clean" as the rule that ends it. 3) State has to live on disk, not in context. The model forgets everything between runs, so an MD file or a knowledge graph holds what is done and what is still open. Each run reads it and writes back to it, which lets a loop pick up again after days. 4) The lower the verification bar, the safer the loop. Boring, repetitive checks like a stale version string or a missing test are trivial to verify, so a loop runs them with little risk while the operator is away. Judgment-heavy work is loopable too, but only as far as the checker can confirm the result. Let's look at how an unattended loop fails in two ways. 1) It reports done when nothing is actually verified. The separate checker exists to prevent it, but it merges code faster than anyone reads it, so over weeks, the team stops understanding its own codebase while every check stays green. Green tests say the code passed the tests, not that anyone knows what shipped. Someone still has to read what the loop merges. 2) The checker keeps a running loop honest, but it only catches failures inside a run. The harness around the loop, like the prompts, tools, and checks wrapped around the model, still drifts and breaks in production as models change. That repair loop is usually run by hand based on observability traces. My co-founder wrote a detailed walkthrough (with code) on making that harness repair itself, where a failing trace gets diagnosed, the fix is verified against the exact input that failed, and the failure is locked as a regression test so it cannot recur. Read it below.

🚨 @Karpathy predicted the power of the "LLM Wiki." Google just formalized it. Meet Open Knowledge Format (OKF): a vendor-neutral standard for giving foundation models the curated context they need. I can genuinely see this replacing Notion, Obsidian, or traditional wikis for developer teams, and the reason comes down to bookkeeping. Traditional wikis fail because humans inevitably abandon the tedious work of updating them. As Andrej Karpathy pointed out recently, LLMs don't get bored. They don't forget to update a cross-reference, and they can touch 15 files in a single pass. OKF standardizes the interoperability layer so agents can actually do that heavy lifting autonomously. Because the format is minimally opinionated, it doesn't dictate what you write, it just dictates how it's structured. You get: → Human-readable documents that live right alongside your code in version control → Cross-links that map out complex entity relationships without needing a graph database → A system that survives moving between different tools and organizations There is no complex compression scheme. No central registry. If you can cat a file, you can read it. If you can git clone a repo, you can deploy it. This is how we stop rebuilding context pipelines from scratch every time a new model drops. Announcement spec file in 🧵↓

GLM-5.2 is Fully Open, Frontier Intelligence Belongs to Everyone Today, the sudden restriction of certain frontier models is deeply regrettable. At a time when access to frontier models is abruptly cut off for non-technical reasons, we are even more convinced of one thing: science should be global. The path to AGI (Artificial General Intelligence) must never be enclosed by high walls. We have always believed that AGI should be the cornerstone for all of humanity to collaboratively explore the boundaries of intelligence and solve complex challenges, rather than a privilege monopolized by a few rules and subject to revocation at any moment. In the face of external blockades and restrictions, our attitude is one of radical openness. Frontier intelligence must remain open-source, accessible, and buildable, serving every dedicated developer. GLM-5.2 is Zhipu's most capable open-source model to date. It not only supports a truly usable 1M context window but also maintains a continuous lead in the independent completion of long-horizon tasks, providing solid foundational support for building complex agent applications. It also continues to be our main engine for creating the strongest domestic coding model. Tonight at 5:21—at this special moment—GLM-5.2 will officially be available to all GLM Coding Plan users (including Lite / Pro / Max). The API will also go live next week. A step closer to frontier intelligence for everyone. The future of AI is open, and it is for the people. ModelKey: GLM-5.2

A dev got so frustrated watching his AI agent write 500 lines for a 5-line problem that he built a fix. He called it Ponytail. Named after the guy every team has - long ponytail, oval glasses, been there longer than the version control. You show him fifty lines; he looks at them, says nothing, and replaces them with one. Now your agent does the same. Before writing anything, it looks for a reason not to. 80-94% less code. 47-77% cheaper. 3-6x faster. The best code is the code you never wrote. GitHub Repo: github.com/DietrichGebert/po…

Claude Code's creator said something that stopped me cold: "I don't prompt Claude anymore. I write loops - and the loops do the work. My job is to write loops." Most developers are still crafting the perfect prompt. in 30 minutes Boris reveals his actual daily Claude Code setup. Claude Code loops dynamic workflow Worth more than a $500 vibe-coding course Watch it. Then read this - everything you need to know about loops to actually apply what he says ↓ Bookmark both. This is your weekend.

The missing design piece for Codex.

In twelve months, EVERY company will be running a Company Brain. The teams who build it this year will spend the next year compounding. Everyone else is going to play catch up. Here's what it actually is. You connect your Slack, your GitHub, HubSpot, all your tools into one intelligence layer, then build the org chart around it: a main brain up top, a fleet commander running the agent fleet, specialist sub-agents handling execution. The reason it works is change management basically disappears. Your team already lives in Slack. You're just adding agents to the room they're already in. You NEED to start building yours now. In a year this will stop being an advantage and will become table stakes.

兄弟们，有个狠人刚搞了个工具，能把PDF秒转成极其干净的Markdown，速度达到100页/秒，你敢信？🤯 不用显卡、不花API钱、没有乱七八糟的解析过程。只有原始、好用的数据，真的爽。 这玩意能干啥？我给你拆开看看： 1️⃣ 表格？完美提取，数据一点不丢 2️⃣ 破损布局？自动修复，烂页面变整洁 3️⃣ 嵌套数据？结构化清理，不绕弯子 4️⃣ 扫描稿？乱七八糟直接变可读 跟你说，这不是小打小闹的升级。这玩意儿一上线，九成的手动数据清理工作都要被干翻，一夜之间没人再熬夜扒数据。 这个工具叫OpenDataLoader，而且完全开源，免费拿走。 仓库在这： 🔗 github.com/opendataloader-pr…

Alguien en GitHub compartió realmente un montón de proyectos gratuitos que son absurdamente buenos. Muchas de sus capacidades ya pueden reemplazar directamente ese software por el que estás pagando mensualmente. 1. TradingAgents Marco de trading cuantitativo multi-agente con IA github.com/TauricResearch… 2. LibreChat Una interfaz que integra ChatGPT, Claude, Gemini y otros múltiples modelos github.com/danny-avila/Li… 3. HyperFrames Motor de generación de video open source de HeyGen github.com/heygen-com/hyp… 4. Fincept Terminal Versión open source del terminal de Bloomberg github.com/Fincept-Corpor… 5. MoneyPrinterTurbo IA que genera videos cortos con un solo clic github.com/harry0703/Mone… 6. Agentic Inbox Asistente de correo con IA open source de Cloudflare github.com/cloudflare/age… 7. VoxCPM Herramienta de clonación de voz con IA github.com/OpenBMB/VoxCPM 8. Flowsint Herramienta open source de análisis de inteligencia OSINT github.com/reconurge/flow… 9. agent-skills Biblioteca de habilidades de código para Claude github.com/addyosmani/age… 10. Nango Plataforma open source de integración de APIs github.com/NangoHQ/nango estos no son proyectos de juguete. Mucho del software por el que aún pagas mensualidades ya tiene reemplazos open source en GitHub hechos por alguien. Las cosas realmente potentes, muchas están escondidas en GitHub.

RT @ghumare64: If you want to go from AI beginner → AI builder & engineer, don’t just watch tutorials. Build from great open-source repos.…