Grok 4.20 Reasoning just took the #1 spot on the BridgeBench reasoning benchmark. 🔥 Beating GPT-5.4, Claude Opus 4.6, Google Gemini and others. Week after week, Grok keeps climbing across benchmarks. 🚀 https://t.co/WnBNrvbQdV

California is trying to pass a bill that would criminalize investigative journalism with misdemeanors, $10,000 fines, imprisonment, and content takedown. The proposed bill is titled AB 2624 and was made after I exposed mass fraud by immigrant groups in America. Under AB 2624, government-funded entities like the Somali “Learing” Daycare centers would be protected from being exposed if they operated inside California. The enemy truly is within. When our politicians would rather protect fraudsters and illegal migrants, it’s time for us to stand up or face mass oppression from the traitors who “rule” over us.

It is telling that those who oppose Elon Musk's investment in South Africa can offer nothing but hollow insults and cheap propaganda. The claim that Starlink is a tool for colonization isn't just daft, it reveals a hilarious level of cognitive shallowness. It proves that the critics don't even understand the technology they are arguing against. You cannot stop Musk's brilliance or the massive contributions he's making to humanity. We are simply witnessing a man who refuses to bend to a crooked system that everyone else has already bowed to. There is no reason to celebrate a status quo built on clear racism and systemic corruption.

Last month Sen. Blackburn released a discussion draft of the TRUMP AMERICA AI Act, a 291 page behemoth of AI regulations. From ending fair use for AI training data, to studying AI company nationalization -- it's a disaster. My and @blainedilli's latest: https://t.co/UaA9TUPGMf

@SharavArora @garrytan It took me two months to "raise" the AI to do https://t.co/kiuZ7QXLzb The bleeding edge understands this. Normies do not. Newer AI needs to be "taught" like you teach a child.

Peter Steinberger, creator of OpenClaw, on why AI agents still produce "slop" without human taste in the loop: "You can create code and run all night and then you have like the ultimate slop because what those agents don't really do yet is have taste." Peter is direct: raw capability without direction still produces mediocre output. "They are spiky smart and they're really good at things, but if you don't navigate them well, if you don't have a vision of what you're going to build, it's still going to be slop. If you don't ask the right questions, it's still going to be slop." Great AI-assisted work is defined by the human guiding it. @steipete describes his own creative process when starting a new project: "When I start a project, I have like this very rough idea what it could be. And as I play with it and feel it, my vision gets more clear. I try out things, some things don't work, and I evolve my idea into what it will become." Most people skip this part entirely, front-loading everything into a single prompt and wondering why the result feels hollow. "My next prompt depends on what I see and feel and think about the current state of the project." Each step informs the next. The work itself is the feedback loop. "But if you try to put everything into a spec up front, you miss this kind of human-machine loop. And then I don't know how something good can come out without having feelings in the loop — almost like taste." The agentic trap is what happens when you remove yourself from the process too early.

Thank you so much guys :D I will be able to push 20x more bugs to prod thanks @shivamhwp @jxnlco https://t.co/iWHwy1FZaY

Thank you so much guys :D I will be able to push 20x more bugs to prod thanks @shivamhwp @jxnlco https://t.co/iWHwy1FZaY

Got a doodle for your next project laying around? Turn it into working software using @GoogleAIStudio and Nano Banana. Watch us vibe code a weather-responsive outfit selector app from a single, hand-drawn sketch: https://t.co/PT4XOeniu3

Back further in history: single cell versus multicellular life mech battles https://t.co/NfOJKBv7Zj

Marcus Hutchins, the guy famous for stopping the WannaCry Ransomware, probably has the best take on Mythos doing vulnerability research https://t.co/ZdknLKnmhO

NEW WORLD RECORD: 18-year-old sprint phenom Gout Gout has clocked a stunning 19.67 time in the 200m run, surpassing Usain Bolt’s legendary mark. https://t.co/0Ib8IewQmY

https://t.co/MTFBIEpFla

https://t.co/MTFBIEpFla

Most genomic AI models use fixed rules to process DNA into chunks, imposing arbitrary boundaries on a sequence with its own biological structure. @arnavshah0, @victor_ljz, and team developed dnaHNet, a tokenizer-free foundation model that learns its own segmentation from scratch, supervised by @_albertgu, @genophoria, and @BoWang87.

We conducted cyber evaluations of Claude Mythos Preview and found that it is the first model to complete an AISI cyber range end-to-end. 🧵 https://t.co/gd9hi0Ve55

Tesla alone sold 117,300 EVs in the US — more than ALL other EV makers combined (99,099). GM + Hyundai + Toyota + Rivian + Ford + Lucid + BMW + VW + everyone else… still couldn’t beat Tesla’s single-quarter numbers. Tesla outsold the entire rest of the industry by over 18,000 vehicles.

It’s fascinating how everyone who has something negative to say about Elon Musk bases it entirely on politics or recycled false accusations. They completely ignore the actual reality: the groundbreaking technology and engineering breakthroughs that only Elon Musk has managed to deliver — reusable rockets, global satellite internet, electric cars that actually changed the industry, Neuralink, Optimus, and more. Critics love to hate the man, but they have zero answer for the impossible things he’s achieved. What does that tell you?

Memo: OpenAI Chief Revenue Officer Denise Dresser says Anthropic is "grossing up rev share with Amazon and Google" and overstating its "run rate by roughly $8B" (@haydenfield / The Verge) https://t.co/9qBEtsz5Tr https://t.co/7Jso88EFmi 📥 Send tips! https://t.co/wlNZvXuhJs

🚨 NOW: The FBI is RAIDING the home of a 20-year-old man who threw a molotov cocktail at the home of OpenAI CEO Sam Altman Over a DOZEN federal agents are executing this search warrant. The suspect's motive was supposedly due to strong anti-AI sentiments, and other AI CEOs were also on his hit list.

Writer's block happens to everyone so we made our prompt box smarter. less typing more building - tab your way to making your idea reality on @GoogleAIStudio today :) https://t.co/hQq3pm20kG

As AI agents accelerate coding, what is the future of software engineering? Some trends are clear, such as the Product Management Bottleneck, referring to the idea that we are more constrained by deciding what to build rather than the actual building. But many implications, like AI’s impact on the job market, how software teams will be organized, and more, are still being sorted out. The theme of our AI Developer Conference on April 28-29 in San Francisco is The Future of Software Engineering. I look forward to speaking about this topic there, hearing from other speakers on this theme, and chatting with attendees about it. We’re shaping the future, and I hope you will join me there! It is currently trendy in some technology and policy circles to forecast massive job losses due to AI. Even if they have not yet materialized, these losses certainly must be just over the horizon! I have a contrarian view that the AI jobpocalypse — the notion that AI will lead to massive unemployment, perhaps even rioting in the streets — won’t be nearly as bad as dire forecasts by pundits, especially pundits who are trying to paint a picture of how powerful their AI technology is. Among professions, AI is accelerating software engineering most, given the rise of coding agents. According to a new report by Citadel Research, software engineering job postings are rising rapidly. So if software engineering is a harbinger of the impact AI will have on other professions, this expansion of software engineering jobs is encouraging. Yes, fresh college graduates are having a hard time finding jobs. And yes, there have been layoffs that CEOs have attributed to AI, even if a large fraction of this was “AI washing,” where businesses choose to attribute layoffs to AI, even though AI has not changed their internal operations much yet. And yes, there is a subset of job roles, such as call center operator, that are more heavily impacted. Many people are feeling significant job insecurity, and I feel for everyone struggling with employment, whether or not the cause is AI-related. And many other factors, such as over-hiring during the pandemic and high interest rates, have contributed to the slowdown in the labor market, and the notion that AI is leading to unemployment is oversimplified. In software engineering, I see a lot of exciting work ahead to adapt our workflows. It is already clear that: (i) As AI makes coding easier, a lot more people will be doing it. (ii) Writing code by hand and even reading (generated) code is not that important, because we can ask an LLM about the code and operate at a higher level than the raw syntax (although how high we can or should go is rapidly changing). (iii) There will be a lot more custom applications, because now it’s economical to write software for smaller and smaller audiences. (iv) Deciding what to build, more than the actual building, is becoming a bottleneck. (v) The cost of paying down technical debt is decreasing (since AI can refactor for you). At the same time, there are also a lot of open questions for our profession, such as: - In the future, what will be the key skills of a senior software engineer? And for junior levels, what should be the new Computer Science curriculum? - If everyone can build features, what skills, strategies, or resources create competitive advantage for individuals and for businesses? - What are the new building blocks (libraries, SDKs, etc.) of software? How do we organize coding agents to create software? - What should a software team look like? For example, how many engineers, product managers, designers, and so on. What tooling do we need to manage their workflow? - How do AI agents change the workflow of machine learning engineers and data scientists? For example, how can we use agents to accelerate exploring data, identifying hypotheses, and testing them? I’m excited to explore these and other questions about the future of software engineering at AI Dev. I expect this to be an exciting event. Please join us! [Original text: The Batch newsletter.] https://t.co/i4bQevDG4i

Every AI answer you trust right now has unchecked logic. Most tools retrieve text and summarize it, but none of them verify whether the output is actually true. One wrong source in a financial memo and your credibility is gone. Every reasoning step should be auditable before it reaches you. MiroMind solved this. → We tried it on a real research task. Evaluate a chip startup across patents, funding, competitors, and technical depth. That kind of work normally takes a week across a dozen tabs. The system got through it in hours, pulling from over 300 sources on its own. It cross-referenced claims across SEC filings, patent databases, and pitch materials. Nobody asked it to find problems. It flagged two contradictions between public filings and investor materials anyway, matching claims across documents that don't look anything alike. That only works because every step is checked before the next one runs. → Here's how the verification actually works. > Four roles run in sequence. > Planner maps the full reasoning graph. > Executor retrieves and processes data. > ChainChecker validates each inference step. > Verifier confirms outputs against original sources. The reasoning graph is a DAG (directed acyclic graph), a structure where steps flow forward and never loop back on themselves. That means branches run in parallel instead of one at a time. If a branch hits a dead end, the system backtracks to the last valid node and replans from there. Most retrieval pipelines just push through bad inferences. This one actually stops. The point isn't the architecture. The point is that nothing reaches the output without being traced back to a source. → That traceability is the actual product. Click any conclusion and walk the full chain back to the raw document. Every claim links to where it came from. It also integrates live market data and returns forecasts with actual numbers behind them, not qualitative summaries. Those numbers are traceable too. They market "300 steps to 99% cumulative certainty." The real value isn't the number. It's that every one of those steps is visible. If you can't audit the reasoning, the confidence score is meaningless. This is where the entire industry is heading. The next generation of AI tools won't compete on fluency. They'll compete on verifiability. If verification-first architectures become the standard, the trust model around AI changes completely.

https://t.co/39gUStV4GN

Process Reward Agents for Steering Knowledge-Intensive Reasoning paper: https://t.co/3JPG5C99Xx https://t.co/dRCKq3AOkM

Typeless v1.2.0 for macOS & Windows is live! 🚀 Think and speak. Completely hands-free. Speak freely without holding down keys. Just press your shortcut once to start Dictate, Translate, or Ask Anything. Press your main shortcut to finish. #Typeless #macOS #Windows

How many nines is 100% uptime? https://t.co/wbil9kP2BP

How many nines is 100% uptime? https://t.co/wbil9kP2BP

🚩LongCat-Next INT4 is now available on @huggingface ! Congrats @meituan @Meituan_LongCat, a very competitive multi-modality model! https://t.co/WDnEn6gkGG, quantized by AutoRound, a flagship quantization tool created by @IntelAI!

trying to figure out which open model to run with my pi agent https://t.co/AAJEgNc9NW

trying to figure out which open model to run with my pi agent https://t.co/AAJEgNc9NW

WildDet3D Scaling Promptable 3D Detection in the Wild paper: https://t.co/LNvH47YJAK https://t.co/bARdQWZMHT