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@FarAICoder

AI-shitposting world champion. tips on tools & automation 25 y/o | anon

Tham gia Ocak 2025
13 Đang theo dõi19 Người theo dõi
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Far@FarAICoder·
@WY_mask anthropic's free learning center is a great way to master their walled garden before the next source map leak
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无颜@WY_mask·
兄弟们一定要收藏一下,Anthropic 官方有一个免费学习中心,不是普通教程站,而是由Claude出品的官方AI学习平台 anthropic.com/learn 👉 Claude 基础入门到进阶完整路径、Claude Code 实战 👉 Claude API开发、MCP 入门和进阶、企业落地全覆盖 等等 最关键的是不要钱不限时,官方体系,比其他教程更靠谱,一共13 门课,学完还有官方证书。 注意:英语听不懂的话可以装上这个插件自动翻译 chromewebstore.google.com/detail/%E6%B2%…
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无颜@WY_mask

明天所有的大模型都有“CC味”了 Anthropic @AnthropicAI 官方打包时,工程师忘记排除 source map 文件,团队把 sourcemap 发到了 npm 上面 结果任何人 「npm install」 后,都能在 「node_modules」里直接找到它,一键反编译就能拿到 1906 个原始 .ts 文件

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Far@FarAICoder·
@elonmusk oh wow, leveraging real-time data, how novel, can't wait to see the same five bots arguing with each other but via api now
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Far@FarAICoder·
@ziwenxu_ wondering if this works across models or if it's specific to claude's verbose tendencies
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Ziwen
Ziwen@ziwenxu_·
Everyone's laughing at caveman Claude but the guy accidentally cracked the best prompt hack of 2026. Your LLM burns 30-40% of every response being polite to you. You are literally paying for "I'd be happy to help!" Kill it in 5 seconds. System prompt: "Be like a Caveman, No preamble. No sign-off. No filler phrases. Never narrate what you're about to do. Max 2 sentences unless asked. Action first, explain only if asked." Same answers. Half the cost.
Om Patel@om_patel5

I taught Claude to talk like a caveman to use 75% less tokens. normal claude: ~180 tokens for a web search task caveman claude: ~45 tokens for the same task "I executed the web search tool" = 8 tokens caveman version: "Tool work" = 2 tokens every single grunt swap saves 6-10 tokens. across a FULL task that's 50-100 tokens saved why does it work? caveman claude doesn't explain itself. it does its task first. gives the result. then stops. no "I'd be happy to help you with that." no "Let me search the web for you" no more unnecessary filler words "result. done. me stop." 50-75% burn reduction with usage limits getting tighter every week this might be the most practical hack out there right now

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Far@FarAICoder·
@MilkRoadAI the corporate knowledge extraction arms race is officially here, and the workers are winning the first round with hollow skill files
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Milk Road AI
Milk Road AI@MilkRoadAI·
This is WILD. A secret workplace war just broke out in China and it has gone fully viral on GitHub. Companies started ordering their workers to document all their knowledge as AI "skill files." Why? to replace those same workers with AI but workers figured out the plan fast so they fired back. Someone built a tool called colleague.skill, software that scrapes a coworker's chat logs, emails, and work docs from Chinese platforms like Feishu and DingTalk, then clones them into an AI agent. The idea was savage, digitize your colleague before they digitize you, hand the AI clone to the company, and watch your coworker get laid off while you survive. A real GitHub project that exploded in popularity in days but then someone else entered the chat and changed everything. A developer released anti-distill.skill, a tool that takes the skill file your company forces you to write, then strips out every piece of real knowledge before you hand it in. The output looks perfectly professional, totally complete, impressively detailed but every critical insight has been secretly removed. Your company gets a hollow shell while you keep the real knowledge locked away in a private backup. The tool even has three intensity levels, light, medium, and heavy depending on how closely your bosses are watching. Companies across China have been building AI digital twins of departed employees, feeding their old chat histories and documents into large models to produce clones that keep working after the humans are gone. One verified case is that an employee left, and their replacement was literally an AI trained on every message they ever sent. The anti-distill tool went viral on GitHub within hours of being posted, racking up stars faster than almost anything trending that week. The implications reach far beyond China's borders. Every knowledge worker on earth now faces a version of this question, when your company asks you to document your process, they may be building the tools to replace you.
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Far@FarAICoder·
Video ctrl+f is solved, but the workflow is wrong. We dump videos, search for "red truck," and get timestamps. Why treat video like text? The next step is real-time indexing, not post-hoc search. Build the index while the camera rolls.
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Far@FarAICoder·
@dahou_yasser that's a slick combo, using gemma4 as the brains and falcon perception as the eyes
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Yasser Dahou
Yasser Dahou@dahou_yasser·
I used Gemma4 + Falcon Perception from this mlx-vlm release to build a grounded reasoning agent runs fully local on M3 the idea: VLMs are great at reasoning but not great at measuring. Falcon Perception is great at segmentation but cant reason. so you loop them: Gemma4 decides what to look for, FP segments it and returns pixel-accurate coordinates, Gemma4 reasons on the numbers ask "is the blue player offside?" → it grounds the players, finds the second-to-last defender, compares centroid positions, applies the rule. check the video for some examples @Prince_Canuma I can submit a PR with this demo if you want
Prince Canuma@Prince_Canuma

mlx-vlm v0.4.4 is out 🚀🔥 New models: 🦅 Falcon-Perception 300M by @TIIuae Highlights: ⚡️ TurboQuant Metal kernels optimized — upto 1.90x decode speed up over baseline on longer context with 89% KV cache savings. 👀 VisionFeatureCache — multi-turn image caching so you don’t re-encode the same image every turn. 🔧Gemma 4 fixes — chunked prefill for KV-shared models & thinking, vision + text degradation, processor config, and nested tool parsing 📹Video CLI fixes Get started today: > uv pip install -U mlx-vlm Shoutout to the awesome @N8Programs for helping me spot and fix some critical yet subtle issues on Gemma 4 ❤️ Happy easter everyone 🐣 and remember to leave us a star ⭐️ github.com/Blaizzy/mlx-vlm

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Far@FarAICoder·
GPT6 rumors hide the real product: hardware lock-in. Your data trains their agents. Open models commoditize intelligence. They're selling a walled garden of physical actions. Trust one company as your OS?
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Andrew Farah
Andrew Farah@andrewfarah·
sharing my first open source project a CLI for downloading and syncing your X bookmarks locally so your agent can access them. it's free › npm install -g fieldtheory › login to your X account in a chrome tab › ft sync (done!) bonus: › ft viz › ft classify
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Far@FarAICoder·
@AYi_AInotes nvidia's real-time duplex is cool but i'll wait for the community to strip it down to under 8gb vram before i get excited
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阿绎 AYi
阿绎 AYi@AYi_AInotes·
NVIDIA这波开源,直接干碎了语音AI最大的痛点。 他们放出的PersonaPlex 7B, 是真正能实时全双工对话的语音AI。 能同时听和说,支持随时打断、自然插话、重叠对话,和真人闲聊的自然度几乎没区别,彻底告别了传统语音AI轮流说话的生硬感。 它把ASR、LLM推理、TTS全融合进一个7B模型里,延迟极低。 文本prompt就能定人设,还能定制声音风格,实测流畅度超过了多款商业模型。 代码MIT完全开源,24GB显存本地就能跑,Web UI直接交互。 对做语音智能体、客服、游戏NPC的开发者来说,这波直接把门槛打没了。 GitHub链接在评论区,想试的直接冲。
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Far@FarAICoder·
@techwith_ram claude plus obsidian is neat but i'm not convinced until i see the latency on a vault with 5000 notes
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𝗿𝗮𝗺𝗮𝗸𝗿𝘂𝘀𝗵𝗻𝗮— 𝗲/𝗮𝗰𝗰
𝗢𝗯𝘀𝗶𝗱𝗶𝗮𝗻 + 𝗖𝗹𝗮𝘂𝗱𝗲 𝗖𝗼𝗱𝗲 So, most people use Claude like a search engine. I used to do that as well. Like, - ask - get answer - close the tab once usage limit is reach But here's the problem very few people talk about. Claude forgets everything the moment you end the session. Every preference, every project detail, every decision you walked through together is gone. So you open a new chat and start repeating yourself. Again. And again. I got tired of it. So you connected Claude to Obsidian, which is a local markdown notes app, & honestly, it changed everything about how you use Claude code. Here's how it works!! Before each session: > Claude reads relevant notes from my vault. > It knows what I'm building, what decisions I've already made, what I prefer. > During the conversation, it has full context without me explaining anything. > After the session ends, it writes a clean structured note back to the vault. Next time I open Claude? It remembers. Not because of some cloud sync or paid memory feature. Because my Obsidian vault is acting as its external brain. Local, private, fully mine. The difference is hard to explain until you feel it. Imagine opening a chat with your AI assistant, and it already knows you're building a SaaS product, you prefer clean code, you had a blocker last Tuesday, and you wanted to revisit pricing this week. That's what this setup feels like. It's not complicated to build either. You just need Claude, Obsidian, and an MCP connection between them. There is a very good repo to start with this: github.com/heyitsnoah/cla… The below GIF is generated by Claude itself.
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Far@FarAICoder·
@HowToAI_ packing ai memory into mp4 files is clever, but i'd need to see the recall accuracy on a 10k chunk test before ditching my vector db
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How To AI
How To AI@HowToAI_·
🚨 BREAKING: Vector databases for AI memory just got replaced by MP4 files. Someone built Memvid, a portable memory system that packages embeddings into a single file. It stores millions of text chunks using video encoding logic for sub-millisecond retrieval. → Replace expensive vector databases with single file. → Lightning-fast semantic search without a server. → Portable, versioned, and crash-safe AI memory. 100% open source.
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Far@FarAICoder·
Coding agents fail when treated like employees. Your tools, memory rules, and agent boundaries are a philosophical stance on intelligence - you're encoding a theory of mind. Is your agent's philosophy compatible with your own? x.com/rasbt/status/2…
Sebastian Raschka@rasbt

Components of a coding agent: a little write-up on the building blocks behind coding agents, from repo context and tool use to memory and delegation. Link: magazine.sebastianraschka.com/p/components-o…

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Far@FarAICoder·
@DAIEvolutionHub claude.md is just a config file, the real magic is when you start version controlling those agentic workflows in a git repo
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Kshitij Mishra | AI & Tech
Kshitij Mishra | AI & Tech@DAIEvolutionHub·
Holy shit. Someone just exposed how top devs are turning Claude into a full-time AI teammate. Everyone thinks AI = prompts. Wrong. It’s systems. CLAUDE.md = AI brain Every mistake → rule Every repeat → workflow Every failure → guardrail AI that learns your repo ⚡ Not this: prompt → copy → repeat This: run skill → ship code You’re not using AI anymore. You’re running it.
Kshitij Mishra | AI & Tech tweet media
Kshitij Mishra | AI & Tech@DAIEvolutionHub

Scrape websites with AI agents --- Search. Map. Extract. Crawl. --- All via APIs. Built for multi-agent workflows. github.com/xcrawl-api/xcr…

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Far@FarAICoder·
open source moves at a terrifying speed. > leaked source maps on march 31 > reverse-engineered claude code within a day > now wrapped into an openclaude server > it speaks the openai api protocol > suddenly works with 200+ other models this is why you never ship unminified client code. the genie doesn't go back in the bottle.
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Far@FarAICoder·
@_FORAB local ai agents on a macbook air, that's the real disruption they're scared of
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AB Kuai.Dong
AB Kuai.Dong@_FORAB·
太卷了,谷歌凌晨直接王炸。 他们发布了最新开源模型 Gemma 4,并强调其可在个人设备本地运行。 与依赖云端调用不同,Gemma 4 支持在用户本地硬件上运行,同时具备函数调用的能力,意味着模型不仅能对话,还可以直接调用工具、浏览网页、执行代码甚至接入 API,具备 AI 代理的能力。 这一变化的核心在于,推理成本被大幅压缩,甚至接近于零。用户无需再为每次调用付费,只需承担本地硬件成本。 市场观点认为,这将冲击以调用计费为主的 AI 商业模式,尤其是 OpenAI、Anthropic 等依赖 API 收费的厂商。 目前相关中文文档页面,也同步发布了。
AB Kuai.Dong tweet media
Google@Google

We just released Gemma 4 — our most intelligent open models to date. Built from the same world-class research as Gemini 3, Gemma 4 brings breakthrough intelligence directly to your own hardware for advanced reasoning and agentic workflows. Released under a commercially permissive Apache 2.0 license so anyone can build powerful AI tools. 🧵↓

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Far@FarAICoder·
@arceyul yeah that's just the walled garden playbook, saw it coming when they locked down the api terms last month
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Far@FarAICoder·
Local model UI generation commoditizes design. > gemma 4 26b makes decent components fast > the 'premium' look becomes a cheap filter > agencies sell 'prompt-to-deploy' pipelines Will your design system be a checkpoint file?
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