Ibraheem Alhashim

Senior Principal AI Researcher @HUMAIN - ex @SCAI_AI - ex @SDAIA_SA - Postdoc at @KAUST_News - PhD @SFU

[Ibraheem Alhashim @ialhashims]

Extremally proud to be part of this project. This is just the start in our journey at SDAIA to help build our national AI capabilities. I am very grateful to the team who have worked on this project and to the SDAIA leadership for their support.

[SDAIA @SDAIA_SA]

#سدايا تطلق النسخة التجريبية من #تطبيق_علّام باللغة العربية الذي يعد الأول من نوعه في المملكة وعمل عليه كوادر وطنية متخصصة ليزود المستخدمين بمختلف المعارف ويرد على استفساراتهم من مئات الملايين من المقالات.

#الذكاء_الاصطناعي_التوليدي

[TechCrunch @TechCrunch]

Nvidia has reportedly agreed to buy Hugging Face, the popular open-source AI hub, for $12.9 billion in a move that would let Nvidia both protect its chip empire and jump back into the cloud business.
https://t.co/v9gaZr4Kdd

[Matthias Niessner @MattNiessner]

📢 GenRecon Code Release 📢

Few images in → complete, high-fidelity 3D scene out!

GenRecon builds a generative prior on full scenes, resulting in unprecedented 3D reconstruction quality.

🔗 https://t.co/1jvzgC7aBO
🌐 https://t.co/sbz1Nb0ptN
📄 https://t.co/VqL3flGBGj

[DailyPapers @HuggingPapers]

NVIDIA just released an optimized GLM-5.2 on Hugging Face

A 753B parameter MoE with 1M context,
quantized to NVFP4 for Blackwell GPUs—
nearly matching FP8 accuracy.

[Zhu Liang @paradite_]

i’m really surprised that people don’t see this.

It’s mathematically true that llms can’t come up with novel ideas, because the whole point of training is to reduce loss, gain rewards so that the model adhere to rules and ground truth.

if you have a model that can come up with novel ideas, it must have high loss during sft or rl.

[Dawn Song @dawnsongtweets]

Everyone says the latest AI agents will be "job-ready" soon, especially after the release of Fable 5 this week. But is that really the case?

Over the past many months, my group and collaborators have been building Agents' Last Exam (ALE), a benchmark designed to test exactly that claim on real digital labor-market work.

My group and collaborators previously have created many of the benchmarks the field runs on, including MMLU, MATH, CyberGym, and ExploitGym. Today, I'm excited to share Agents' Last Exam (ALE): a rolling benchmark that measures whether AI agents can actually perform economically valuable work across a broad range of real-world domains.

With ALE, we evaluated Fable 5, GPT-5.5, Composer 2.5, and other frontier agent systems across more than 1,500 expert-sourced tasks spanning 55 occupations.
The result is both impressive and sobering.

Today's agents can solve a meaningful fraction of professional tasks. But when we look at the hardest tasks, the ones requiring sustained reasoning, deep domain expertise, and reliable execution over long horizons, they are still far from human-level performance.

On ALE's hardest tier, every frontier agent we tested, including Fable 5, achieved a 0% success rate.
The age of useful agents is here.

The age of truly job-ready agents is not.

We hope Agents' Last Exam (ALE) will serve as a new guidepost and north star for developing agents capable of reliably performing economically valuable work across a broad range of domains.
🧵

[François Chollet @fchollet]

Some considerations that many folks seem not to get:

1. It can be a bubble even if the tech works. (For instance, if the tech doesn't have a high-demand use case.)

2. It can be a bubble even if the tech works and has strong product-market fit. (For instance, if the tech cannot be economically viable.)

3. It can be a bubble even if the tech works, has strong product-market fit, and has a path to eventual economic viability. (For instance, if profitability takes too long to achieve or makes margin/competition assumptions that fail to materialize.)

4. It can be a bubble even if the tech works, has strong product-market fit, and is currently highly profitable. (For instance, if demand has a hard ceiling and growth stops once the ceiling is reached.)

5. It can be a bubble even if the tech works, has strong product-market fit, is currently highly profitable, and has unlimited future demand.

Literally all it takes for something to be a bubble is for lots of people to over-enthusiastically bet their money on it, and subsequently get panicky.

Importantly, bubbles can be attached both to things that are completely hogwash, like the Metaverse, and to world-changing developments like the Internet or railways. Bubbles don't care. They're brought into existence by the thoughts and feelings of investors, not by actual tech or products.

"The bubble has burst" doesn't mean "the tech didn't work" or "people stopped using the tech." It only means that people got panicky, investor money dried up, and valuations collapsed. Internet adoption didn't stop in 2000.

[Amazon Web Services @awscloud]

More AI-generated code doesn't make your team faster. It might actually slow you down.

[HUMAIN @HUMAIN]

One year ago today, HUMAIN was launched with a bold vision to build the full AI stack.

Today, we continue building the future of AI, transforming ambition into real-world impact.

From vision to intelligence.
From ambition to action.
From possibility to scale.

This Is Year One.

#HUMAIN #TheEndOfLimits

[Keenan Crane @keenanisalive]

A bunch of folks have been building machine learning models that turn a photograph into a 3D environment made of Gaussian splats (read: blobs of color floating in space).

Cool technology & a very admirable effort. But marketing these as "world models" seems wrong.

More accurate would be to say that they are a riff on the broader class of image-conditioned 3D generators, with a somewhat different flavor of condition image and output representation.

As far as world modeling, they don't make great predictions about how the natural world looks or behaves. (Even for, say, a chair behind a table.)

Again: I love the technology. Super cool creative stuff. I don't love the marketing and hype around it.

[Zain Shah @zan2434]

Imagine every pixel on your screen, streamed live directly from a model. No HTML, no layout engine, no code. Just exactly what you want to see.

@eddiejiao_obj, @drewocarr and I built a prototype to see how this could actually work, and set out to make it real. We're calling it Flipbook. (1/5)

[Jitendra MALIK @JitendraMalikCV]

I see every week on X an announcement or demo which implies that robotic manipulation has been solved. The only reason I don't believe it is because manipulation had already been solved last week by somebody else! So may I propose the "5 year old paired comparison test" ? At the next conference let's set up a number of tables to which you can bring your robot hardware. Next to it we will have another table where there will be a 5 year old child. In parallel we will try 100 different manipulation tasks that a neutral person has chosen- we could start with "pick up anything" - only household objects (e.g. as might be found in a typical American home) will be used, and we compare the performance of your robot with that of the 5 year old. Can you pick up a coin? Or a book? Or untwist a bottle top? Or insert any plug into a matching socket? Rotate one face of a Rubik's cube? Until your robot can do all the "open world manipulation" that a 5 year old kid can, some humility is in order.

[ARC Prize @arcprize]

Announcing ARC-AGI-3

The only unsaturated agentic intelligence benchmark in the world

Humans score 100%, AI <1%

This human-AI gap demonstrates we do not yet have AGI

Most benchmarks test what models already know, ARC-AGI-3 tests how they learn

[سلمان بن عبدالعزيز @KingSalman]

نحمد الله سبحانه وتعالى أن أكرمنا بإتمام صيام شهر رمضان المبارك وقيامه، ونسأل الله أن يديم علينا أمننا واستقرارنا، وأن يحفظ أبطالنا البواسل على الثغور والحدود في مختلف القطاعات العسكرية والمدنية.
وكل عام وأنتم بخير، وبلادنا في عز ورفعة.

[HUMAIN @HUMAIN]

Wishing you a blessed and joyful Eid Al-Fitr. 🌙

كل عام وأنتم بخير.
نتمنى لكم عيد فطر سعيد، وأياماً مليئة بالخير والمسرات. 🌙

#HUMAIN #TheEndOfLimits

[واس الأخبار الملكية @spagov]

#مجلس_الوزراء: الموافقة على تسمية عام (2026م) بـ(عام الذكاء الاصطناعي).
#واس

[سلمان بن عبدالعزيز @KingSalman]

نستذكر في هذا اليوم المجيد تأسيس دولتنا المباركة، التي أقامها الأجداد على كلمة التوحيد، وتحقيق العدل، وجمع الشتات تحت راية واحدة؛ بما حقق بفضل الله تعالى الأمن والازدهار.

[Google DeepMind @GoogleDeepMind]

We used Gemini 3.1 Pro to build a realistic city planner app. 🏙️

Watch how the model tackles complex terrain, maps out infrastructure, and simulates traffic to generate a high-quality visualization.

[سلمان بن عبدالعزيز @KingSalman]

نهنئكم بشهر رمضان المبارك، ونسأل الله تعالى أن يبارك لنا ولكم وللمسلمين في هذا الشهر الفضيل، وأن يتقبل منا ومنكم صالح الأعمال، وأن يديم على بلادنا الأمن والرخاء.

[Andrej Karpathy @karpathy]

A few random notes from claude coding quite a bit last few weeks.

Coding workflow. Given the latest lift in LLM coding capability, like many others I rapidly went from about 80% manual+autocomplete coding and 20% agents in November to 80% agent coding and 20% edits+touchups in December. i.e. I really am mostly programming in English now, a bit sheepishly telling the LLM what code to write... in words. It hurts the ego a bit but the power to operate over software in large "code actions" is just too net useful, especially once you adapt to it, configure it, learn to use it, and wrap your head around what it can and cannot do. This is easily the biggest change to my basic coding workflow in ~2 decades of programming and it happened over the course of a few weeks. I'd expect something similar to be happening to well into double digit percent of engineers out there, while the awareness of it in the general population feels well into low single digit percent.

IDEs/agent swarms/fallability. Both the "no need for IDE anymore" hype and the "agent swarm" hype is imo too much for right now. The models definitely still make mistakes and if you have any code you actually care about I would watch them like a hawk, in a nice large IDE on the side. The mistakes have changed a lot - they are not simple syntax errors anymore, they are subtle conceptual errors that a slightly sloppy, hasty junior dev might do. The most common category is that the models make wrong assumptions on your behalf and just run along with them without checking. They also don't manage their confusion, they don't seek clarifications, they don't surface inconsistencies, they don't present tradeoffs, they don't push back when they should, and they are still a little too sycophantic. Things get better in plan mode, but there is some need for a lightweight inline plan mode. They also really like to overcomplicate code and APIs, they bloat abstractions, they don't clean up dead code after themselves, etc. They will implement an inefficient, bloated, brittle construction over 1000 lines of code and it's up to you to be like "umm couldn't you just do this instead?" and they will be like "of course!" and immediately cut it down to 100 lines. They still sometimes change/remove comments and code they don't like or don't sufficiently understand as side effects, even if it is orthogonal to the task at hand. All of this happens despite a few simple attempts to fix it via instructions in CLAUDE . md. Despite all these issues, it is still a net huge improvement and it's very difficult to imagine going back to manual coding. TLDR everyone has their developing flow, my current is a small few CC sessions on the left in ghostty windows/tabs and an IDE on the right for viewing the code + manual edits.

Tenacity. It's so interesting to watch an agent relentlessly work at something. They never get tired, they never get demoralized, they just keep going and trying things where a person would have given up long ago to fight another day. It's a "feel the AGI" moment to watch it struggle with something for a long time just to come out victorious 30 minutes later. You realize that stamina is a core bottleneck to work and that with LLMs in hand it has been dramatically increased.

Speedups. It's not clear how to measure the "speedup" of LLM assistance. Certainly I feel net way faster at what I was going to do, but the main effect is that I do a lot more than I was going to do because 1) I can code up all kinds of things that just wouldn't have been worth coding before and 2) I can approach code that I couldn't work on before because of knowledge/skill issue. So certainly it's speedup, but it's possibly a lot more an expansion.

Leverage. LLMs are exceptionally good at looping until they meet specific goals and this is where most of the "feel the AGI" magic is to be found. Don't tell it what to do, give it success criteria and watch it go. Get it to write tests first and then pass them. Put it in the loop with a browser MCP. Write the naive algorithm that is very likely correct first, then ask it to optimize it while preserving correctness. Change your approach from imperative to declarative to get the agents looping longer and gain leverage.

Fun. I didn't anticipate that with agents programming feels *more* fun because a lot of the fill in the blanks drudgery is removed and what remains is the creative part. I also feel less blocked/stuck (which is not fun) and I experience a lot more courage because there's almost always a way to work hand in hand with it to make some positive progress. I have seen the opposite sentiment from other people too; LLM coding will split up engineers based on those who primarily liked coding and those who primarily liked building.

Atrophy. I've already noticed that I am slowly starting to atrophy my ability to write code manually. Generation (writing code) and discrimination (reading code) are different capabilities in the brain. Largely due to all the little mostly syntactic details involved in programming, you can review code just fine even if you struggle to write it.

Slopacolypse. I am bracing for 2026 as the year of the slopacolypse across all of github, substack, arxiv, X/instagram, and generally all digital media. We're also going to see a lot more AI hype productivity theater (is that even possible?), on the side of actual, real improvements.

Questions. A few of the questions on my mind:
- What happens to the "10X engineer" - the ratio of productivity between the mean and the max engineer? It's quite possible that this grows *a lot*.
- Armed with LLMs, do generalists increasingly outperform specialists? LLMs are a lot better at fill in the blanks (the micro) than grand strategy (the macro).
- What does LLM coding feel like in the future? Is it like playing StarCraft? Playing Factorio? Playing music?
- How much of society is bottlenecked by digital knowledge work?

TLDR Where does this leave us? LLM agent capabilities (Claude & Codex especially) have crossed some kind of threshold of coherence around December 2025 and caused a phase shift in software engineering and closely related. The intelligence part suddenly feels quite a bit ahead of all the rest of it - integrations (tools, knowledge), the necessity for new organizational workflows, processes, diffusion more generally. 2026 is going to be a high energy year as the industry metabolizes the new capability.

[واس الأخبار الملكية @spagov]

#مجلس_الوزراء: تشكيل لجنة وزارية دائمة تُعنى بالتنسيق لمواءمة الجهود والخدمات المقدمة لمرضى طيف التوحد، ودراسة التحديات التي تواجه المرضى وأسرهم، وإيجاد الحلول اللازمة لمعالجتها.
#واس