Denis Pitcher

The Afroman Trial. -Cops raid Afromans house for bullshit reasons. -Steal money, break his door, fuck his house up. -No criminality found whatsoever, no charges at all pressed on Afroman. -Afroman spends the next 3 years making songs that make fun of all the officers involved by name, even using footage of the raid from his own CCTV cameras. -Songs had titles like "Randy Walters is a son of a bitch" and "Lick Em Low Lisa" accusing one of the officers of being a lesbian and sleeping with the other officers wives. -During the raid one officer looked like he was about to eat some lemon pound cake sitting on Afromans counter, Afroman made a whole album calling the officer fat. -The cops get mad and file a lawsuit for defamation. -Afroman turns up to court in a whole American flag suit. -Officers performatively mald and cry while listening to the songs really trying to oversell how badly the songs upset them. -One officer was suing because Afroman made a whole song about him saying he was fucking the officers wife. When the officer was asked if Afroman was really fucking his wife, he said "I don't know". Nuking his own case and establishing that there is a non-zero chance that Afroman might actually be fucking his wife. -As his only witness for the trial, Afroman brought a deputies EX FUCKING WIFE. -The jury ruled completely in favour of Afroman. This entire thing has been a great win for free speech and absolutely fucking hilarious.

We’ve trained a multimodal AI model to turn routine pathology slides into spatial proteomics, with the potential to reduce time and cost while expanding access to cancer care.

A corporate lawyer named Zack Shapiro just published the most important essay I've read on how AI transforms a profession. It's about law. Every word applies to medicine. His core thesis: AI is not a democratizing force. It is an amplifier. It amplifies excellent judgment into exceptional output. It amplifies poor judgment into faster mistakes. In law, the concept of the "10x lawyer" never existed — not because talent didn't vary, but because the structure of legal work prevented the best lawyers from delivering returns proportional to their ability. Complex deals required teams. Delegation diluted the senior partner's judgment through layers of associates with less context. Time was a hard ceiling. One person simply could not do two hundred hours of work in two weeks. AI removes that ceiling. A senior lawyer with AI can now hold an entire transaction in a single context window, cross-reference six interrelated agreements simultaneously, and produce a complete markup with strategic memo in one working session. What took a five-person team three weeks now takes one excellent lawyer three days. The same structural constraint has existed in medicine. A physician's clinical judgment — the pattern recognition built over thousands of patient encounters — has always been diluted by the production mechanics of care delivery. You can only see so many patients. You can only hold so much of a complex case in working memory. You can only read so many studies. The system compresses the signal from your best physicians into the same throughput as your average ones. AI changes that equation. A physician with excellent clinical judgment using frontier AI models can now hold an entire patient's longitudinal history in context, cross-reference it against current literature, generate and pressure-test a differential, and draft a management plan — in the time it previously took to review the chart. The cognitive bandwidth constraint that made all physicians look roughly equivalent in throughput is dissolving. This is where it gets uncomfortable. The gap between the best and the average is about to become visible in ways the old system could hide. And the market — whether that's patients, health systems, or payers — will reprice accordingly. But here's what most physicians are missing. The physicians dismissing AI because their EHR's built-in tools are underwhelming are making a critical error. They're evaluating a domain-specific wrapper and concluding that AI itself isn't ready. That's like a lawyer dismissing AI because Harvey's interface didn't impress them, while their competitor is using frontier models natively to produce categorically different work. The frontier models are already good enough. The bottleneck was never the technology. The bottleneck is whether you have the judgment to use it and the curiosity to start. Shapiro's full essay is worth reading regardless of your profession. The structural dynamics he describes — the re-sorting of an entire market around individual capability rather than institutional prestige — are not unique to law.

As someone who's already at the spot where agents write 100% of my code, it's crazy to me to see that 28% of responses here think they still won't be there a year from now

An NVIDIA powered farming machine uses Al vision and precision lasers to eliminate weeds in milliseconds without herbicides and without harming crops, a potential shift toward chemical free agriculture

Tradfi guys will bend over backwards to tell you that the incumbent financial system is the safest, most secure and most efficient one that could ever be conceived. Yet they don’t even know that the entire finance backend is made in COBOL, a coding language made literally in the late 1950s for business data processing.

The vertical integration pattern is the story here, and it keeps repeating out of Shenzhen. Sipeed built PicoClaw to run on their own $9.90 LicheeRV Nano boards. The company that wrote the software also manufactures the hardware it runs on. That’s the part worth paying attention to. The numbers are absurd. 10MB of RAM vs OpenClaw’s 100MB+. Boot time of 1 second vs 500+ seconds. 95% of the codebase was generated by an AI agent, then human-refined. They shipped it in a single day and hit 5,000 GitHub stars in four. Zoom out and you see the DeepSeek pattern applied to infrastructure. DeepSeek took frontier model training and compressed the cost by 10-20x. PicoClaw takes AI agent orchestration and compresses the hardware requirement by 40x. Both came out of China. Both are open source. Both make Western incumbents look like they’re optimizing for the wrong variable. Sipeed is one of China’s most active RISC-V hardware companies, partnered with Canonical on Ubuntu, shipping everything from $10 dev boards to AI cameras. China’s government is actively pushing RISC-V adoption as an alternative to ARM and x86. PicoClaw running on RISC-V means AI agents on chips with zero Western IP licensing dependencies. The $399 Mac mini comparison in the tweet is funny, but undersells what’s actually at stake. What happens when a billion IoT devices in emerging markets can each run a personal AI agent for under $10 in total hardware cost? That’s the market Sipeed is building toward. And they control both sides of the stack.

I don't know why this week became the tipping point, but nearly every software engineer I've talked to is experiencing some degree of mental health crisis.

Still bid 69,420