K // Andrea Kregar (@andreakregar) header
K // Andrea Kregar (@andreakregar) profile picture

Dreamer // Passionate Father_Husband_Developer // #BeingMyself #BeingBold // cit. You Are Your Own Best Competitor // #HNRG CTO in @AlteaFederation

Italy
Joined January 2011
Photo by K // Andrea Kregar (@andreakregar)Photo by K // Andrea Kregar (@andreakregar)Photo by K // Andrea Kregar (@andreakregar)Photo by K // Andrea Kregar (@andreakregar)Photo by K // Andrea Kregar (@andreakregar)Photo by K // Andrea Kregar (@andreakregar)Photo by K // Andrea Kregar (@andreakregar)Photo by K // Andrea Kregar (@andreakregar)Photo by K // Andrea Kregar (@andreakregar)
A book you absolutely can't miss during this time of change #ontologypipeline #jessicatalisman #knowledge
16
K // Andrea Kregar retweeted
You can now use oMLX in LM Studio! Download the new engine and use it with MLX models you already have. Excited to work with with @jundotkim and @huggingface to make this happen! 👾🐧🚀
42
54
18
613
63,871
K // Andrea Kregar retweeted
I recently wrote about why we need to treat models as insider risks, with security controls sitting outside the model itself. Today, we're putting that principle into practice by expanding Project Perception with new capabilities to help continuously assess agent risk, adapt permissions, and contain threats. blogs.microsoft.com/blog/202…
264
307
111
1,758
363,217
K // Andrea Kregar retweeted
Introducing Clairic, from Cloudflare. A System One extraction modeI, capable of extracting, computing, estimating, and forecasting data from unstructured inputs. cfl.re/4zIgfpH
53
200
105
1,664
166,755
K // Andrea Kregar retweeted
My conversation with @alexandr_wang, chief AI officer of Meta. 0:00 The vision behind Muse 1:32 Discovering the potential of personal AI agents 3:34 Three years of therapy in one AI session 5:46 Building Muse in 7 months 9:16 Why Meta bet on personal AI 16:51 Mark Zuckerberg's obsession with improving products 20:15 How Meta knew Muse was ready to launch 23:32 Why great products need a single point of view 25:56 Making Meta cool again with memes 34:38 Building trust with AI agents 38:43 The future of human ambition 45:22 The call from Mark Zuckerberg that changed everything 52:13 Leaving Scale AI for Meta 55:19 Rebuilding Meta's AI lab from scratch 58:13 From founder to coach 1:05:55 The future billionaires at Alexandr's 19th birthday party 1:08:04 Diamond mining vs. building skyscrapers 1:09:21 Why Alexandr has more than 200 direct reports Includes paid partnerships.
109
253
90
2,416
906,267
K // Andrea Kregar retweeted
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel. - Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all. - Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract. - Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code. - And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while. The funnel is driven by a combination of factors: 1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel. 2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel. 3. Access. Free-tier, paid-tier, internal. So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code. But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along. So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions. TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
2,242
5,906
2,281
44,088
6,905,422
K // Andrea Kregar retweeted
Today marks a new chapter for Windows, as we bring unmetered intelligence to every desk and every home, and make every PC a place where agents can work securely on your behalf. Some highlights of what we announced: • MAI-Code-1.1 Flash: 137B parameter coding model w/ 256K context window, which is now optimized to run on your PC! • GitHub Copilot now hands off work to local models like MAI-Code-1.1 Flash, helping projects cost a lot less without sacrificing quality. • With Hybrid Intelligence, Copilot can now take action directly on the PC and keep sensitive work on your device. • And with Code in Copilot, you can essentially build any software you need on your desktop, without any cloud token spend, and it’s just super at it. You’re no longer limited to what’s in an app store! Your PC becomes an infinite software factory. • Security is foundational to all this, which is why we are also bringing together Windows and Agent 365 so agents can work within secure boundaries on-device, including MXC a local sandbox for agent execution. Windows becomes your secure agent box! • All this comes to life on a new generation of devices, like Surface Laptop Ultra, powered by NVIDIA RTX Spark. Can’t wait to see what you build with all this.
569
967
314
7,645
763,320
K // Andrea Kregar retweeted
We are excited to announce that World Labs is joining @AMD. The research and technical breakthroughs we have achieved since our founding in 2024 have given us a clear vision for AI’s potential to solve problems in the spatial and physical world. Accelerating the future of spatial and physical intelligence requires scaling our efforts, scaling our reach, and getting closer to the hardware.
234
521
347
5,479
1,461,593
K // Andrea Kregar retweeted
HydraFusion is now available in the GitHub Copilot app!
👋 Project HydraFusion is now available in the GitHub Copilot app and @code. Choose it like any other model. Behind the scenes it routes your task across multiple models to draft, critique, revise, or escalate, then hands you one result. github.blog/changelog/2026-0…
8
14
2
119
11,383
K // Andrea Kregar retweeted
“𝙄 𝙣𝙚𝙚𝙙 𝙩𝙤 𝙙𝙖𝙧𝙚!” 🔥 Elisa Longo Borghini animated the Women Elite Road Race and got rewarded with a well-deserved bronze medal 🥉 Pure class! 🎩 #Montréal2026
5
19
1
187
11,677
K // Andrea Kregar retweeted
I wrote about the state of AI, why I’m concerned about the next few years, and the choices we need to make to keep the future in humanity’s hands. An Alien Mind: openai.com/index/an-alien-mi…
962
2,513
1,091
15,254
7,691,687
K // Andrea Kregar retweeted
DeepSeek v4 Flash experimental with vision running (fast) on an m5 max computer and analyzing an image. The implementation for Metal / CUDA / ROCm is finished, just doing the last tests today before releasing it.
24
29
11
533
62,204
K // Andrea Kregar retweeted
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. spr.ly/6012B1x7YI
63
187
140
783
413,329
K // Andrea Kregar retweeted
I've got a pretty clear picture now of where we're headed next year, when Fable-class models become ubiquitous and cheap, and every enterprise is flooded with hundreds of new Fable-class AI employees. Spoiler: They create a constitutional legal system. yegge.ai/essays/fences-not-s…
51
46
41
612
98,622
K // Andrea Kregar retweeted
New: A map of the most important skills in AI Engineering.
577
4,142
484
24,593
7,008,337
K // Andrea Kregar retweeted
my favorite way to explain people herdr is to just open a new workspace, launch an agent and tell it to: read `herdr --skill` then split 10 other panels that make my computer look like some scifi hacker terminal from the movies
177
150
52
3,376
513,794
K // Andrea Kregar retweeted
📣 Kimi K3 is now available in GitHub Copilot for @code! Try Kimi Moonshot's latest open-weight model for agentic coding, now hosted by @FireworksAI_HQ. 📖 Learn more: github.blog/changelog/2026-0…
110
342
36
3,186
1,609,758