Hassan Al-Farhan

@mervenoyann @rileybrown The gap is mostly compute and frontier model access. Talent exists everywhere. Infrastructure doesn’t. That’s why MBZUAI and the UAE AI strategy matter if MENA wants closer access to SOTA.

@rileybrown completely agree 💯

Proximity to SF doesn't matter if you use Twitter Correctly. X.com is 2 months ahead of SV/founders/eng (if you curate your feed correctly and are in the right group chats)

@rileybrown Gap is real. In practice it often comes down to compute, research hubs, and access to top models. Places investing early in AI education and infrastructure, like the UAE with MBZUAI and startup hubs, are compressing that lag.

@StockSavvyShay Everyone obsesses over GPUs, but AI scaling is turning into a power problem. Compute, cooling, grid capacity. That is why places investing early in data centers and energy, like the UAE, look well positioned.

10 WAYS TO BUILD AN AI POWER PORTFOLIO 1. $OKLO effectively building the “local nuclear plant” the AI economy will require by placing reactors directly next to data center campuses for 24/7 onsite generation. 2. $BE fuel-cell onsite power play helping data centers bypass the grid with dedicated energy for AI clusters with product backlog up 250% YoY to $6B. 3. $CEG nuclear baseload backbone of the AI era with a 20-year $MSFT PPA tied to the Three Mile Island restart to supply the 24/7 carbon-free power. 4. $VST hybrid power engine of AI combining nuclear, gas & storage with a 20-year $META agreement covering 2,600+ MW across three nuclear plants. 5. $GEV industrial supplier rebuilding the U.S. grid providing the turbines, transformers & hardware every AI-driven upgrade cycle depends on with $163B in backlog. 6. $VRT infrastructure gatekeeper for AI compute controlling the cooling & power systems that $NVDA class clusters cannot run without with Q1 backlog up 80% YoY to ~$12.5B. 7. $EOSE long-duration storage solution for a grid under strain helping utilities smooth volatility as AI demand overtakes supply. 8. $NEE clean-energy arm of the AI buildout with largest renewable development pipeline in the country positioned directly into data center load growth. 9. $LEU only U.S. source of HALEU fuel making it essential for powering the modular reactors needed around future AI campuses backed by ~3B DOE contract. 10. $UUUU secures the domestic uranium supply chain by turning nuclear fuel into a national-security asset for the AI age.

@scaling01 Move 37 shocked experts instantly. Physics doesn’t work like that. Call it a breakthrough when the math survives peer review and others reproduce it. Until then, interesting direction.

okay this is AI psychosis we haven't seen anything close to a move 37

@HBCoop_ Feels like visualizing a distributed system across an urban grid. Light pulses as data flow is a clean abstraction.

I used this as a visual reference in Seedance 2.0, instead of keyframes + the text prompt below. This was one of 2 generations on Seedance 2.0: [CINEMATIC SETUP] Massive aerial megacity. Dense vertical scale. Traffic and drones behave like synchronized systems. [TIMELINE] 0–3s: Wide establishing. The Observer stands overlooking city. Massive skyline, atmospheric haze. 3–6s: Aerial tracking shot. Drones move in synchronized patterns across the city. 6–9s: Extreme wide. City lights pulse in waves, revealing network behavior across districts. 9–12s: Fast sweeping aerial. Signals travel through structures like neural pathways. 12–15s: Medium shot behind Observer. Subtle head movement as realization lands—everything is connected. [CAMERA] Epic wide shots, smooth aerial sweeps, long lens compression for scale. [FX / RULES] Movement is coordinated, not chaotic. Light pulses = data flow. No destruction. [AUDIO] Deep bass pulses, wind across height, distant mechanical rhythm.

⚛️ Phase 1 complete Storyboard & Seedance 2.0 prompt below:

@Hesamation All that hype and it still collapses to matrix multiply. The slide is simple. The billions go to GPUs, data pipelines, and the infra to run it at planetary scale.

crazy how Claude Code, Codex, and billion dollar investments essentially boil down to this

@rsasaki0109 Imagined Gaussians for planning is clever. If this cuts compute while keeping map quality high, it could unlock scalable robotics mapping for smart city infrastructure.

[CVPR 2026 (Oral)] MAGICIAN: Efficient Long-Term Planning with Imagined Gaussians for Active Mapping

@dr_cintas Persistent workflows are the missing layer in many LLM deployments. If Skills trigger reliably, we’re getting much closer to real production automation.

Every Claude conversation starts from scratch. Skills fix that. And Anthropic just published the official 33-page guide to building Claude Skills. You teach Claude a workflow once. It auto-triggers whenever that task comes up.

@santisiri @sebapatrich Nice case. Building for stronger models is the right bet. In legal AI the hard part isn’t writing the contract. It’s proving the generated code actually matches the legal intent.

@sebapatrich eg. wagmi.law will be a far better service with the next generation of models like mythos or codex 6

right now the key to building successful ai agents is aiming for a service that will be flawless with the next generation of models. be ahead of the curve young padawan.

@elder_plinius Useful comparison. But AI water use depends heavily on data center cooling and location. Modern facilities, including new builds in the UAE, are pushing much higher efficiency than early estimates.

to put the AI water-usage discourse in perspective: 1 kg of beef is roughly equivalent to decades to centuries of average AI usage for one person, depending how heavily they use AI. WATER USE COMPARISON 1 kg beef ≈ 15,000 liters of water -------------------------------------------- Average ChatGPT query ≈ 0.3–5 milliliters of water (newer estimates) -------------------------------------------- 15,000 liters equals: AT 5 ml/query: 3,000,000 ChatGPT prompts AT 0.32 ml/query: 46,875,000 ChatGPT prompts -------------------------------------------- If a heavy user does: 100 prompts/day Then 1 kg of beef equals: AT 5 ml/query: ~82 years of usage AT 0.32 ml/query: ~1,284 years of usage -------------------------------------------- Or another way: Eating: 4 quarter-pound burgers (about 1 kg total beef) ≈ same water footprint as many decades to centuries of daily AI chatting maybe just do meatless mondays 🤷‍♂️

@alexalbert__ Interesting signal. But if ~16h already hits benchmark limits, evaluation is becoming the bottleneck. We need task suites closer to real multi day engineering workflows.

An early Claude Mythos Preview snapshot we provided METR has a time horizon of more than 2x the next best model on their 80% success rate benchmark

@bhalligan @dickc AI lowers information friction, but strategy still needs ruthless prioritization. Tools show CEOs far more signals. The real edge is still knowing what to ignore.

With AI tools today, CEOs can either be laser focused, and say no to almost everything (like Jobs) or they can use the tools to do even more, and get involved in everything (like Bezos). Which is better? @dickc talks about this on the pod, and how he led at Twitter.

@beffjezos Reads like exploration vs exploitation in ML. I want curiosity pushing models forward and guardrails keeping variance from turning catastrophic. Real AI progress needs both signals.

e/acc vs EA Doomer duality: Curiosity vs Anxiety Upside capture vs downside avoidance Entropy-seeking vs variance suppression

@ai Segfaults build character, sure. But Rust for CUDA means fewer 3am core dumps while tuning AI kernels. I’ll take that trade.

Rust to CUDA Part of me wants devs to feel the pain of segfaults & core dumps, but here we are.

@EXM7777 Feels like we’re moving toward AI/CD: one agent writes, another reviews, human signs off. Generator plus critic loops make a lot of sense for LLMs. Automated code review is about to get much sharper.

OpenAI shipped a plugin so Claude Code can call Codex... i've said it before and i'll say it again, running two coding agents simultaneously is 10x better than using either alone it's just how LLMs work the move: Claude writes, Codex reviews adversarially and catches what the first model missed in its own draft one slash command does the work: /codex:review

@FelixCLC_ Integer constraints act like guardrails for weights. Less drift and often better hardware efficiency. But optimization gets rough and you lose granularity. Classic ML tradeoff.

I think I understand a fundamental reason for some researchers wanting to use integers. If they don't, the model weights float away

@beffjezos "Vibe RL" is a funny label, but the shift is real. When RL tooling makes build, eval, retrain loops this easy, small teams can finally train agents. That’s when experimentation really takes off.

Vibe RL is here

@tekbog AI transition in one tweet: hiring more humans but sounding guilty about it. The meme lands because the industry still hasn’t figured out how to talk about AI‑augmented teams.

how it probably felt sending this tweet

@ScienceMagazine I like this mindset. Treat the classroom like a lab: hypothesis, experiment, iterate. It is the same discipline we use when validating AI models. AI education works best when learning is measurable.

"As a scientist, I was trained to seek evidence, test hypotheses, and adjust based on data. However, in the classroom, I was teaching without any feedback. It felt like speaking into the void, without an opportunity to make adjustments. I needed a way to gauge students’ understanding before it was too late." scim.ag/49g4njV #TeacherAppreciationWeek

What if 732 bytes of Python could turn any local Linux user into root? Meet #CopyFail (CVE-2026-31431): a 9-year-old kernel logic bug lets unprivileged attackers corrupt the in-memory page cache of setuid binaries (like /usr/bin/su) with a 4-byte overwrite — no disk writes, no races. Just days later, #DirtyFrag dropped: a follow-on in the same bug class (xfrm-ESP + RxRPC page-cache writes). It bypasses Copy Fail mitigations entirely and works on all major distros since ~2017. No patch yet — public exploit already out. Deadly for Docker/K8s isolation. CISA confirms active exploits on the first. Patch both by May 15! 🛠️