A YC startup just open sourced Whiteboard, a canvas where Claude Code and Codex draw diagrams of the code they write so you can actually see what your agent built
Repo: https://github.com/devdotfast/whiteboard
GitHub - devdotfast/whiteboard: open-source IDE for thoughtful software design
First time I put a bot on a memecoin, I did what everyone does. Told it to find a coin, watch the chart, buy the dip, sell the top. Basically asked a machine to gamble better than I gamble.
It worked, a little. Then it didn't. Then it worked again. Classic trading bot arc, exciting for a week, humbling by week three.
Somewhere in there I stopped asking "how do I make it trade better" and started asking a different question. What if the bot didn't have to guess at all.
That question turned into three completely different systems, running on three completely different logics. One still gambles. One never bets on a coin's direction at all, it just gets paid regardless of who's right. One doesn't even try to think for itself, it just copies whoever already figured it out.
Here's all three, in the order I actually built them, including the one that's currently costing me money because of a bug I only found last week. One run each, not a hundred, so treat every number below as "this is what happened this time," not a promise.
I'm not trading anymore. I'm just copying whoever Grok Bot decides is smarter than me, and apparently that's working, $2,740 → $12,386.
it doesn't guess the market. it just watches which wallets already guessed right, and copies them before I can even read the ticker.
scanned close to 8,000 wallets in that window alone. it's not looking for a setup, it's looking for a wallet that's already moving, then it's in the same trade within milliseconds.
five copies did almost half the profit:
long SOL 3x, +$619, copied in 31ms
long ETH 4x, +$726, copied in 24ms…
This article probably shouldn't be free.
I built an 8-figure+ net worth in the AI era. I just leaked the entire method.
My most detailed and best article of the month.
My playbook for starting an AI business, earning real cash, scaling & reinvesting: https://t.co/y2RZE6CnIy
She is 18 built an AI agent with live memory and Anthropic bought it for $2.2M - and at Stanford shared how to repeat it from scratch:
00:40 - how Opus 5 builds an agent with live memory from scratch in 60 minutes
18:44 - the live memory system replaced 500 Anthropic engineers
39:42 - from first agent to $2.2M from Anthropic
after watching I gave Opus 5 one prompt - an agent with live memory appeared in an hour I sent an invoice to Anthropic and got a check for $120k.
save & watch - the article below shows how to build an AI agent with live memory and sell it to Anthropic for $2.2M.
At the time of writing, Artie is a team of 17. Every one of us works alongside Hermes, an open-source AI agent harness from @NousResearch. It runs on a single physical machine in Germany, has a different personality for each team, and dreams at night.
This is how it got that way.
A few months ago, one of our engineers (Ani, @anirudhsriramzz) was running Hermes for himself. Just a personal setup, and he talked to it over WhatsApp.
Around the same time, Robin, our CTO, came back from a hackathon where a friend had shown him a whole roster of agents running their work. The question he brought back was simple: how do we make that available to everyone at Artie, and not only the engineers?
We looked at OpenClaw first. It's good. But it was easy to watch an agent wander off and do something nobody asked for, and we wanted guardrails before we wanted scale. So we picked Hermes and started thinking about what the guardrails should be.
Obsidian finally got a CLI.
Highly reccommend setting up your AI second brain ASAP. https://t.co/o34J3fvWTN
A research agent is only as good as the search layer behind it!
I built a Deep Research Agent using LangChain Deep Agents with a search-first architecture designed around grounded, source-backed research.
The agent plans the research in real time, breaks the work into smaller steps, searches across the web, reasons over the retrieved sources, and produces a fully cited answer.
For this project, I used Liner as the search and grounding layer.
Liner returns raw, structured search results with titles, URLs, descriptions, and dates.
Some guy made a python library of self-improving algorithmic trading AI agents
Then gave it away for free:
{{IMAGE_0}}
It's called ATLAS.
And it applies these 3 concepts to Financial Markets:
{{IMAGE_1}}
18 Month Backtest Results:
From Sept 2024 to March 2026 Equity Curve.
{{IMAGE_2}}
Get it here free:
Want to learn Algorithmic Trading Strategies (that actually work)?
On September 3rd, we are hosting a free workshop to help you get started with algorithmic trading with Python.
Register here (630+ registered):
https://
learn.quantscience.io/join
{{IMAGE_3}}
THIS IS THE ARTICLE I WASN'T GOING TO PUBLISH. everyone's using Grok Bot at 10% of its actual SEO potential. these 20 prompts are the other 90%. i'm giving every single one away for free.
https://pbs.twimg.com/media/HQFoXyjasAAA5mr.jpg
i built this system after 14 years of managing local SEO for home services businesses. plumbers. HVAC companies. lawyers. cleaning companies. i've seen what moves rankings and what wastes time.
these 20 prompts are the ones that actually move the needle. these are the actual prompts you paste into Grok Bot and get real output from.
save this. you'll need it.
before you run a single prompt - load your business context
"Here is everything you need to know about my business before we start any SEO work. Reference this every time I ask you to run an audit, build a strategy, or analyze competitors. Never ask me for this information again.
WHOEVER BUILT THESE AI HACKING TOOLS WAS NOT PLAYING
And the crazy part?
They don’t just scan for vulnerabilities.
They can actually simulate attacks—> chain attack paths—> validate what they find.
Here are the 3 tools:
→ Decepticon runs 16 autonomous red team processes inside a hardened Kali Linux sandbox.
→ Pentest-Swarm-AI launches multiple attack paths across your network perimeter in parallel.
→ Strix performs real application pentests and validates vulnerabilities with working exploits.
AI powered security testing is getting seriously wild.
REPO BELOW
{{IMAGE_0}}
Here are all the links
Decepticon:
https://
github.com/PurpleAILAB/De
cepticon
…
Pentest-Swarm-AI:
https://
github.com/Armur-Ai/Pente
st-Swarm-AI
…
Strix:
https://
github.com/usestrix/strix
Follow
@MAXdeg0
if you find it useful
https://t.co/M6s8jFE4OA
https://t.co/3jaqUblHC8
https://t.co/uMO9AQM2t5
done, it's open source now
iOS phone farm - register real iPhones, watch + control them live in the browser, schedule tiktok on a postgres-backed scheduler.
free, self-hosted, apache-2.0.
https://t.co/COLryoAtwK
https://t.co/N8XGsrTiqn
don't buy the tool sellers, WDA works proof 2:
{{IMAGE_0}}
30 Search requests/min = up to 43,200 searches a day.
And Fetch comes with it. All FREE.
Search + Fetch are fundamental primitives for agents. Cost shouldn’t be the reason anyone thinks twice about implementing them.
Love seeing
@MonidHQ
put TinyFish Search + Fetch into the hands of more agents!
Grab your free API Key and try it out at
10 AI Trading GitHub Repos You Should Have on Your Radar
AI is moving into trading — from LLM-powered analysts to reinforcement learning and automated quant research.
If you're interested in AI + finance, these 10 open-source repos are worth exploring.
A multi-agent LLM framework that simulates a trading firm with specialized analysts, traders and risk-management agents.
🔗 https://github.com/TauricResearch/TradingAgents
An open-source financial reinforcement-learning framework for training AI agents on trading and portfolio-allocation tasks.
🔗 https://github.com/AI4Finance-Foundation/FinRL
An AI-native quantitative trading infrastructure combining ML, reinforcement learning, LLM-ready workflows and risk controls.
🔗 https://github.com/AI4Finance-Foundation/FinRL-Trading
Microsoft's open-source AI-oriented quantitative investment platform for research, modeling and strategy development.
🔗 https://github.com/microsoft/qlib
I have been working on a package that sets up the HackerGadgets AIO Board. This package supports the RPi CM4/5. This package is designed for Debian if used on another distro YMMV. I wanted to make this package to take some of the headache of configuring everything if you so choose.
To install the package in Debian sudo apt update && sudo apt --install-recommends install hackergadgets-uconsole-aio-board -y && sudo apt install meshtastic-mui -y to install everything
If you would like to just install apps piecemeal here’s a list of them I’ve preconfigured:sdrpp-browntar1090pygpsclientmeshtastic-mui - You’ll need to install the full hackergadgets package for this to work.
The following programs will be installed and configured with app launcher the the start menu where applicable:
After you have accurate system time you need to set the RTCs time by issuing. sudo hwclock -w
Reboot and system time will automatically be set.
Argumentum, c'est un matériel ludique qui peut se jouer de plein de façons différentes.
La règle pour débuter, pour découvrir les cartes, c'est celle de l'école des menteurs.
Nous rassemblons dans cette page les règles que nous avons imaginées et toutes celles que vous nous conseillez.
N'hésitez pas à proposer une variante, une amélioration ou un nouveau jeu!
F*ck it
I'm giving away the internal doc we use at Boom on every $25-$60K operating system build.
Steal the system:
Map every workflow before touching a tool, and time the manual hours (if you follow me, you already know this)
Sort your stack into 3 buckets: absorb, keep, kill
every entity gets exactly one write path, in writing, before anything gets built
Consolidate one department at a time. Automate nothing yet
4 agents go on top in a specific order (intake always first)
Route simple tasks to cheap models, save frontier models for reasoning. Cuts token spend 80%+…
Learn 95% of Codex in 28 minutes
These are the 7 knowledge work capabilities...
inside Codex, the super-app
00:00 Intro
02:19 Capability 1 - Full File Access
07:41 Capability 2 - Persistent Memory
10:46 Capability 3 - Plugins
13:52 Capability 4 - Skills
19:22 Capability 5 - GPT Image Access
21:03 Capability 6 - Browser and Computer Use
23:58 Capability 7 - Automations
25:31 Bonus Feature - Chronicle
27:21 Summary
Bluetooth Low Energy (BLE) is widely used for smart home, wearable and IoT sensors. This guide provide a simple but useful tool that send your BLE data to MQTT servers, which is the underling mechanism for popular IoT solution.
In this guide we will introduce two GL.iNet BLE gateway, GL-X750 Spitz and GL-S1300 convexa S which has built-in BLE modules and how to use them to forward your BLE data to the cloud based on MQTT protocol, including GL.iNet GoodCloud and AWS IoT.
Both GL-X750 and GL-S1300 comes with a Silicon BLE 5.0 module with the following specs. It connects to the main board using UART. The same module support BLE and Zigbee. However you need to change the firmware on the module (not the gateway). There is no way to change the firmware from BLE to Zigbee or vice versa on the air. So you have to select the correct module when you purchase the correct product.
An AI rewrote a million lines of code in 11 days.
This was a real production runtime. Bun, the JavaScript engine that millions of apps quietly run on, went from 535,000 lines of code in one language to more than a million lines in another, all of it inside two weeks. Close to a year of work for a team, by hand.
And the thing that rewrite produced is already on your PC. Not in a repo you would have to go dig up, not behind a signup. Ten seconds to prove it:
strings ~/.local/bin/claude | grep -m1 'Bun v1'
# → Bun v1.4.0
The runtime that million-line rewrite produced is already inside the tool you code with.
What pulled it off was the shape of the work itself. The models were ordinary, the ones you already run. Wire them as a graph instead of a line, and one job at a time becomes a fleet running at once.
That is the layer almost nobody is taught to see.
Most people still line their agents up and wait. You type a request, wait for it, fix what came back, then ask the next thing.
validation is the product Sep 1
Finding bugs is easy; proving they’re real is the hard part. Sep 1