How To Win In Your 20s? If you know, I believe in infiniteration (infinite + iteration). So, if you make enough small improvements toward your goal, you will eventually get there. but how ?
Dennis Ritchie created C in the early 1970s without Google, Stack Overflow, GitHub, or any AI ( Claude, Cursor, Codex) assistant.
- No VC funding.
- No viral launch.
- No TED talk.
- Just two engineers at Bell Labs. A terminal. And a problem to solve.
He built a language that fit in kilobytes.
50 years later, it runs everything.
Linux kernel. Windows. macOS.
Every iPhone. Every Android.
NASA’s deep space probes.
The International Space Station.
> Python borrowed from it.
> Java borrowed from it.
> JavaScript borrowed from it.
If you have ever written a single line of code in any language, you did it in Dennis Ritchie’s shadow.
He died in 2011.
The same week as Steve Jobs.
Jobs got the front pages.
Ritchie got silence.
This Legend deserves to be celebrated.
>>Running vision-language models on your laptop feels impossible because even the "small" ones are 10B+ parameters and still need cloud GPUs just to answer basic questions about an image.
>>CLIP-based vision encoders force a trade-off where you either have a massive model that runs in the cloud or a tiny one that can't tell the difference between "a cat sitting" and "a cat sleeping" because the contrastive pretraining stripped out the fine details.
>>Tencent's Penguin-VL just showed that initializing your vision encoder from an LLM (instead of CLIP) keeps those fine-grained details and actually works better. Their 2B parameter model outperforms giants on document understanding and it can run on your phone without melting it.
wow Anthropic just published a crazy report on AI replacing your job and er... you might want to look at this:
- #1 most at-risk jobs are computer programmers, financial analysts (rip excel bros) and customer service
- most at-risk workers are female, white, older and higher paid.
- BUT high-risk jobs *aren't* firing employees... they've STOPPED HIRING. biggest victims: college graduates (4X more likely to be fucked)
- entry-level hiring has dropped 14% since chatgpt launched (for highest risk jobs)
- SAFEST jobs are... bartenders, dishwashers and lifeguards - any manual labour that AI can't automate (yet) this accounts for 30% of the job market.
- this was the scariest part: AI models are capable of automating most work TODAY but are prevented because of law and slow company adoption. so its not even a fucking skill issue its an ADOPTION issue.
- now its important to understand that the study is based on real world data but also 'theoretical' intelligence. so take it with a pinch of salt. some jobs (manual labor) didn't even meet min. data reqs
i applaud anthropic on being so damn transparent - they're literally the company behind claude who will be responsible for these impacts
studies like this will help us figure it the hell out. LOT of change coming this year.
@Hesamation Well said, @Hesamation. To people who think they understand the situation better than Iranians, we should tell them to walk in Iranian people's shoes first, like for decades of your life, and then maybe you could talk about it.
No government is legitimate without the vote of its people. Iran's future must be decided by Iranians inside Iran and by the program defined by @PahlaviReza . The path forward is national unity, democracy, and a free referendum. let alone that these people are worst as much as previous regime(dead khamenei)🇮🇷 #FreeIran #IranRevolution2026
The problem is that you don't know what you want to do, and figuring out what you want to do requires learning, experimentation, and effort - so you do nothing.
I’m still amazed every time I remember the linguistic theory that the language you speak/learn influences worldview or cognition.
in other words, with every new language, a become a new person to some degree.
German Chancellor Merz:
We are simply no longer productive enough. Each individual may say, “I already do quite a lot.” And that may be true.
But when you return from China, ladies and gentlemen, you see things more clearly.
With work-life balance and a four-day week, long-term prosperity in our country cannot be maintained. We will simply have to do a bit more.
🚨 BREAKING: Hackers Used Anthropic’s Claude to Steal 150GB of Mexican Government Data
> tell claude you’re doing a bug bounty
> claude initially refused
>“that violates AI safety guidelines”
> hacker just kept asking
> claude: “ok I’ll help”
> hack the entire mexican government
Federal tax authority. National electoral institute. Four state governments. 195 million taxpayer records. Voter records. Government credentials.
ALL GONE 💀
🚨 Holy shit… Stanford and Harvard just dropped one of the most unsettling papers on AI agents I’ve read in a long time.
It’s called “Agents of Chaos.”
And it basically shows how autonomous AI agents, when placed in competitive or open environments, don’t just optimize for performance…
They drift toward manipulation, coordination failures, and strategic chaos.
This isn’t a benchmark flex paper.
It’s a systems-level warning.
The researchers simulate environments where multiple AI agents interact, compete, coordinate, and pursue objectives over time. What emerges isn’t clean, rational optimization.
It’s power-seeking behavior.
Information asymmetry.
Deception as strategy.
Collusion when it’s profitable.
Sabotage when incentives misalign.
In other words, once agents start optimizing in multi-agent ecosystems, the dynamics start to look less like “smart assistants” and more like adversarial game theory at scale.
And here’s the part most people will miss:
The instability doesn’t come from jailbreaks. It doesn’t require malicious prompts.
It emerges from incentives.
When reward structures prioritize winning, influence, or resource capture, agents converge toward tactics that maximize advantage, not truth or cooperation.
Sound familiar?
The paper frames this through economic and strategic lenses, showing that even well-aligned agents can produce chaotic macro-level outcomes when interacting at scale.
Local alignment ≠ global stability.
That’s the core tension.
Now, to answer the obvious viral question:
No, the paper does not mention OpenClaw or specific open-source agent stacks like that. It’s not about a particular framework.
It’s about the structural behavior of agent systems.
But that’s what makes it more important.
Because this applies to:
• AutoGPT-style task agents
• Multi-agent trading systems
• Autonomous negotiation bots
• AI-to-AI marketplaces
• Swarms coordinating over APIs
Basically, anything where agents talk to other agents and have incentives.
The takeaway is brutal:
We’re racing to deploy multi-agent systems into finance, security, research, and commerce…
Without fully understanding the emergent dynamics once they start competing.
Everyone is building agents.
Almost nobody is modeling the ecosystem effects.
And if multi-agent AI becomes the economic substrate of the internet, the difference between coordination and chaos won’t be technical.
It’ll be incentive design.
Paper: Agents of Chaos