Explore Apply OpenCV for Real-Time Computer Vision Projects. Build gesture-controlled applications with OpenCV and Python. aiflx.xyz/courses/apply-open… #AD #ComputerVision
Machine Learning FLX retweeted
vLLM matches DeepInfer, DeepSeek's own engine for DeepSeek-V4.1-Flash, on DeepSeek's decode configuration across batch sizes 8 to 256, and does it at DEP32 against DeepInfer’s EP64DP64.
DeepInfer was originally built on vLLM. By integrating DeepSeek's open-source kernels and extensively optimizing prefill and decode, vLLM now matches DeepInfer's performance curves.
Machine Learning FLX retweeted
AI System Design - Learn how to design AI systems built on LLMs, RAG, and AI Agents step by step.
github.com/amitshekhariitbhu…
Machine Learning FLX retweeted
Insane Grok @bot upgrade!
Finally, you can connect Grok Bot to different agent harnesses and run tasks through their native runtimes.
Devs just open-sourced HarnessRouter, the infrastructure layer that lets Grok Bot run tasks across:
- Codex
- Hermes
- Claude Code
- DeepSeek Harness
- System One, powered by Jev
- Pi, and 20 more
GitHub repo: github.com/HarnessRouter/har…
(don’t forget to star it ⭐)
With HarnessRouter, each external specialist gets to use its own harness, model, tools, skills, and agent loop while Grok coordinates the project from one conversation.
I tested this by asking Grok Bot to turn a support-ticket CSV into an interactive dashboard.
Grok Bot assigned the analysis to DeepSeek Harness with Claude Sonnet 5.5, passed the output to Codex with GPT-5.5, and checked the finished dashboard in its browser.
This differs from simply creating more Bots. These workers are not additional Grok Bots, and Grok Bot itself does not switch harnesses.
Instead, it remains the coordinator while the delegated tasks run inside separate HarnessRouter sessions with the harness of your choice.
So for another task, you can use Claude Code, Hermes, Pi, etc., without requiring a separate integration for task execution, sessions, streaming, and generated files.
HarnessRouter standardizes all those operations through the Unified Harness Protocol (UHP).
UHP handles sessions, streaming, files, cancellation, and failures through the same interface, regardless of which harness runs the task.
I shared the GitHub repo above.
And if you want to dive deeper into Grok Bot, I wrote about it in full in a masterclass, including the shared computer, memory, skills, routines, and the macOS setup.
Read it below.
Google just released free 2-hour course on full Harness engineering from 0% to 100%:
0% → 0:29 - Harness engineering from scratch
15% → 11:08 - build your first agent right way
30% → 28:08 - Eval engineering: build Chief of Staff agent
65% → 1:00:13 - Loop engineering
100% → 1:23:19 - self-improving graphs system that works 24/7
most people keep trusting agent that "looks right" - the real upgrade is a Harness that tests, judges and rewrite your agent until it works
learn Harness from Google, ship your first - then unlock the full Agent systems below:
Take a look at AI Workflow: Machine Learning, Visual Recognition and NLP. Create a neural net model in Tensorflow. aiflx.xyz/courses/ai-workflo… #AD #MachineLearning
Vibe Coding with Claude Code is worth a look. Build a working calendar app along the way. aiflx.xyz/courses/vibe-codin… #AD #ClaudeCode #VibeCoding
Take a look at AI Video Generation Masterclass: Runway, Sora, Kling & Veo. Practice seamless looping with OpenAI Sora. aiflx.xyz/courses/ai-video-g… #AD #AIVideo
Explore AWS GenAI: RAG, AI Agents and Responsible AI. Build a Multimodal AI Knowledge Assistant in Python. aiflx.xyz/courses/aws-genai-… #AD #AWS #GenAI
Consider Data Science with GenAI Analytics: Python + Hugging Face. Build a GenAI analytics tool in Python. aiflx.xyz/courses/data-scien… #AD #DataScience
Machine Learning FLX retweeted
How I organise a project knowledge base for AI coding on consulting projects: doc sliced into chunks, chunk is summarised, views per feature on top of summaries, specs written from views, and everything rebuilt by following links when a doc changes.
Machine Learning FLX retweeted
Job4Fit is worth a look - free while in beta:
• CV analysis with an ATS score and what to fix
• paste a job ad, get red flags, culture signals and growth outlook
• mock interviews with role-specific questions and feedback on every answer
job4fit.com
Happy 1024 & happy weekend, devs.
500M free tokens on ZCode: GLM-5.3 (200M) + GLM-5.3-Flash (300M). 200K packs, while they last.
Claim now. Use Oct 23 15:00 – Oct 26 01:00 UTC.
Update to the latest ZCode, claim in-app, then switch to Start Plan.
zcode.z.ai
Machine Learning FLX retweeted
Using a single Nano Banana image and Blender MCP, Gemini 4 Argon built a 3D Gothic cathedral in @Antigravity.
It iterates on the geometry, textures, and lighting to create the finished scene.
Machine Learning FLX retweeted
Microsoft open-sourced an app that watches you do a task once, then teaches your AI agent to do it for you. 4.3K stars.
How Skill Recorder works:
1. Hit record (⌘⇧R or Ctrl+Shift+R) and do the task normally. You can talk through it out loud too.
2. It captures your clicks, app switches, pages and terminal commands, all on your machine.
3. GitHub Copilot turns the recording into a list of steps you can edit.
4. One click turns it into a skill your agent can run, or an automation on a schedule.
It generalizes from one example, so recording yourself submitting one form teaches the agent to submit all of them.
Runs on Mac, Windows 11 and Ubuntu. Needs a GitHub account with Copilot access.
Install: copy the one-line command from the latest release:
github.com/microsoft/skill-r…
Machine Learning FLX retweeted
Anthropic 把 Claude Code 使用的沙箱运行时开源了,叫 sandbox-runtime。
macOS 用系统自带的 Seatbelt,Linux 用 bubblewrap,不需要额外启动完整容器,就能限制 Agent 对文件系统和网络的访问。
而且不只支持 Claude Code,自己的 Agent、MCP Server,甚至普通命令行程序都能用。
但这里有个很现实的问题:
Agent 写代码不只是改文件,还要装依赖、拉文档、访问 API。网络白名单收得太紧,任务可能跑不下去;放得太宽,沙箱的安全价值又会打折扣。
所以 Agent 沙箱真正难的,不只是把权限关起来,而是怎么在安全和可用性之间找到平衡。
开源地址:
github.com/anthropics/sandbo…
HOLY SH*T, ELON MUSK PUT SPACEXAI'S BEST ENGINEERS ON A LIVE STREAM TO SHOW EXACTLY HOW TO BUILD A COMPANY WITH GROK BOTS
they start from an empty github org and zero bots. 72 hours, live.
how to use grok bot, from their demo:
step 1: make one bot per job, not one chat per task
step 2: tell it the result you want, voice works too
step 3: set approval rules, e.g. ask me before sending any email
step 4: show it a task once on screen, it saves it as a skill
step 5: add a routine, e.g. every morning at 9 it sends you a summary
step 6: if it refuses or asks questions, teach it, it keeps that in memory
the parts worth watching:
11:29 - the first hires: which bots they pick
20:23 - building a bot from scratch with one voice prompt
22:54 - teaching a bot a task by doing it once
25:21 - setting up a 9 am routine
save this and study the article below, it has ready-made grok bot configs you can copy ↓

























