Image created with gemini-3.1-flash-image-preview with claude-opus-4.7. Image prompt: The word ‘TECH’ rendered in large bold letters built from concentric ROYGBIV rainbow bands with violet outside stepping inward to red, the strokes routed like clean right-angle circuit traces with a single ribbon looping into a small node at the end, flat matte screen-print finish on a pale warm off-white background with generous negative space, crisp printed edges, no shadows or gradients, Julio Le Parc kinetic op-art style, 16:9.
In early May, the best superforecasters predicted that, by the end of the year, the longest METR 80% task horizons would reach 3-4 hours. In late May, Claude Mythos achieved that number.
https://x.com/emollick/status/2062235461364445204
How lucky are you to have been born when and where you are? Had Opus 4.8 in Claude Code whip up a new visualization of all humans who ever lived. In addition to being neat, it is an interesting test of combining research, code, design and stats for an AI.
https://x.com/emollick/status/2060165879908749490
Introducing Claude Opus 4.8 \ Anthropic
https://www.anthropic.com/news/claude-opus-4-8
Today, Anthropic engineers on average ship 8x as much code per quarter as they did compared to 2021-2025.
https://x.com/AnthropicAI/status/2062568864240836995
When AI builds itself \ Anthropic
https://www.anthropic.com/institute/recursive-self-improvement
Hyperscaler capital expenditures came in on trend in Q1 2026, continuing the trajectory that projects them spending $770 billion this year and over a trillion dollars in 2027.
https://x.com/EpochAIResearch/status/2060076222873526506
MAI-Transcribe-1.5 is available at $6 per 1,000 minutes of audio via Microsoft Foundry.
https://x.com/ArtificialAnlys/status/2061878498609053909
Today we announced MAI-Thinking-1, a strong generalist and reasoning LLM built from the ground up without distilling third-party models. 97% on AIME 2025; 53% on SWE-Bench Pro; preferred by human raters over Sonnet 4.6 (blind side-by-side). Tech report:
https://x.com/asadovsky/status/2062008312603070891
Super excited to announce seven new world-class MAI models today. They represent what we consider a new era in AI designed to keep you in control and on the frontier. First is our text foundation model, MAI-Thinking-1, exceptionally strong on reasoning and SWE tasks. – It’s a
https://x.com/mustafasuleyman/status/2061880164498428188
MAI-Image-2.5 is here — now #3 on text-to-image and #2 on image-to-image Arena leaderboards, surpassing Nano Banana Pro. Leading image generation. Precise editing. Built for enterprise scale. It delivers strong performance on H100s, enabling deployment on existing
https://x.com/MicrosoftAI/status/2062240400299934143
Building a hill-climbing machine: Launching seven new MAI models | Microsoft AI
https://microsoft.ai/news/building-a-hillclimbing-machine-launching-seven-new-mai-models/
Molmo2 is a CVPR 2026 award candidate paper from @allen_ai Molmo2 is a VLM that supports video pointing, tracking, counting by pointing, and multi image reasoning all in one open model prompt: blue players
https://x.com/skalskip92/status/2062549751246066144
We took another look at the capability gap between open-weight and proprietary models. Since the start of the year, open-weight models have lagged the state of the art by four months.
https://x.com/EpochAIResearch/status/2060451576779886942
MiniMax M3: Frontier Coding, 1M Context, Native Multimodality — All in One Model – MiniMax Research | MiniMax
https://www.minimax.io/blog/minimax-m3
A Functional Taxonomy of World Models – Dr. Fei-Fei Li
https://drfeifei.substack.com/p/a-functional-taxonomy-of-world-models
Introducing Agent Arena: real-world agentic evals at scale. How do you evaluate agents doing actual work? We measure millions of live sessions where real users accomplish real tasks. On Arena, models now get web search, filesystem, and terminal tools to complete complex
https://x.com/arena/status/2062566749418233981
🤔It is time to rethink how we evaluate agent memory 🌍 As agents become longer horizon and more autonomous, memory is no longer just a module for storing past chats. 🛠️ It determines how agents track changing worlds, learn from past actions, revise outdated information, and
https://x.com/liuchen02938149/status/2061842528698311103
CUAs need to move beyond the prevailing single serial agent paradigm, and start being researched, evaluated, and deployed as multi-agent systems. We hope MACU provides useful insights and a reproducible foundation for future research. 💻 Code:
https://t.co/2tmxNMqHb1 📄 Paper:
https://x.com/kohjingyu/status/2062179533009178897
Anthropic Opus 4.8 is new SOTA on ARC-AGI-3 Score: 1.5%, ~$10K ARC-AGI-3 analysis notes: * Opus 4.8 read the environment an abstraction *above* Opus 4.7, as objects & systems, not pictures * Opus 4.8 succeeded on early levels, but still committed to a wrong sub-goal
https://x.com/arcprize/status/2061512025638121516
Claude Opus 4.8: The System Card | Don’t Worry About the Vase
https://thezvi.wordpress.com/2026/05/29/claude-opus-4-8-the-system-card/
I had early access to Opus 4.8. Was impressed by it. Here is Opus 4.8’s one shot of “”create a visually interesting shader that can run in twigl, make it like an infinite city of neo-gothic towers partially drowned in a stormy ocean with large waves”” (this is all done with math)
https://x.com/emollick/status/2060042738637148470
Opus 4.8 Part 2: Model Welfare | Don’t Worry About the Vase
https://thezvi.wordpress.com/2026/06/01/opus-4-8-part-2-model-welfare/
Opus 4.8 vs MiniMax M3 tested both on default settings with the same prompt > Opus one shotted everything in 7 minutes > M3 needed an extra prompt to fix the “”break block”” feature and took 20+ minutes both got super close, judge both and lemme know which one looks better?
https://x.com/notjazii/status/2061407087293313210
The issue affected how Opus 4.8 requests were handled, causing the model to trigger more parallel tool calls than intended. It was unrelated to dynamic workflows.
https://x.com/ClaudeDevs/status/2061501790131265803
Help us produce the most useful work on AI by taking our 5-minute survey:
https://t.co/W2tLu3e4WW (You can sign up at the end to join our compensated user research panel.)
https://x.com/EpochAIResearch/status/2059336781208924566
I think Epoch does a great job benchmarking, but I continue to believe that open weights models are much more fragile, especially out-of-distribution, than their benchmarks indicate. Vibe-wise, I don’t think they were only 3 months behind last year or only 4 months behind today.
https://x.com/emollick/status/2060736941453189622
Intelligence Per Dollar | Tomasz Tunguz
https://tomtunguz.com/tokens-per-result/
State of AI Engineering | Datadog
https://www.datadoghq.com/resources/state-of-ai-engineering/
State tracking is a core pillar of video understanding: it requires identifying entities and events, and mapping how their states evolve over time. Frontier multimodal models are surprisingly bad at it, so we built a benchmark to measure it. Meet VSTAT!
https://x.com/PinzhiHuang/status/2062004108249145442
We’ve added narrations to our long-form content on the Epoch AI website, including reports, Gradient Updates, and topic overviews. Look for the play button.
https://x.com/EpochAIResearch/status/2060096279808745759
We just published internal data on how much of Claude’s development is already being done by Claude: – Over 80% of all code merged into our codebase is now written by Claude – It’s been months since many researchers at Anthropic hand-wrote code – The typical Anthropic engineer
https://x.com/alexalbert__/status/2062580571214389510
We’ve added a CLI for Claude Platform to make every API endpoint runnable from your terminal. Call the Messages API, stand up Claude Managed Agents, pipe results straight into your shell. The ant CLI is well understood by coding agents (Claude Code) using the claude-api skill.
https://x.com/ClaudeDevs/status/2061877343078244459
We’ve reset 5-hour and weekly rate limits for all users on Pro and Max plans. We fixed an issue that caused some Claude Code sessions to spawn excessive parallel subagents, burning through usage faster than expected.
https://x.com/ClaudeDevs/status/2061501787769893055
Lem & Douglas Adams got AI right Presciently Golem XIV (from 1981) has an illustration of the jagged frontier as explained by an AI, Golem (GENERAL OPERATOR, LONG-RANGE, ETHICALLY STABILIZED, MULTIMODELING), discussing itself and a smarter AI (Honest Annie) compared to people
https://x.com/emollick/status/2059847527105462363
Automatic behind the scene routing in user interfaces (instead of model picker) will redistribute value capture and usage towards many more models than just frontier ones (especially towards open-source/smaller/cheaper ones). Because it removes the cognitive load for the final
https://x.com/ClementDelangue/status/2061871024627482964
🚨 MAI-Image-2.5 is now live on fal! 📸 Photorealistic images with natural lighting and accurate skin tones Refined text rendering for branding, packaging, and commercial design Text-to-image and image editing with precise, design-ready control
https://x.com/fal/status/2061920052664820199
Mai-1 thinking: Mid size model, 45b active parameter, MoE, side by side with sonnet 4.6 0 distillation „Microsoft’s first reasoning model”
https://x.com/kimmonismus/status/2061877528781025381
microsoft MAI tech report is a gold mine, one of the most transparent for a model at this scale. this model uses zero synthetic data or distillation from previous models. this means reasoning, agentic behavior, tool use are all learned fully during post-training with no cold
https://x.com/eliebakouch/status/2061965825037254947
This SkillOpt paper from Microsoft is a must-read! (bookmark it) I was a bit skeptical of the results reported in the paper when I shared it a few days ago. However, I managed to integrate it into my agent orchestrator and ran a few experiments. The results are mindblowing.
https://x.com/omarsar0/status/2062204469538881988
Three new @MicrosoftAI models now live on OpenRouter! Launching together: MAI-Image-2.5, MAI-Transcribe-1.5, and MAI-Voice-2. More on each below 🧵
https://x.com/OpenRouter/status/2061894672847671724
As token budgets take on a larger part of operating expenses over time, model routing is the inevitable conclusion. This is also one of the biggest areas of differentiation for the applied AI layer over time. By understanding the different work patterns in your domain, and
https://x.com/levie/status/2061974298760495132
Excited to see the use of GEPA-optimized LLM judges for data filtering in MAI-Thinking-1 model’s pre-training pipeline!
https://x.com/LakshyAAAgrawal/status/2062013650639241403
Give MAI-Code-1-Flash a try and let us know what you think!
https://x.com/pierceboggan/status/2062220583786709163
MAI-Thinking-1 is out! Excited to share what we are building and how climbing from scratch (no distillation) actually works: simple recipes, rigorous science, self-distillation, patience, and great infra. Check out our tech report has the full story of our RL climbs.
https://x.com/HannaHajishirzi/status/2061901432627044430
Super detailed tech report for MAI-Thinking-1, with a ton of info on all stages of the pipeline. I’m surprised so much of this info is released 🙂 Super long thread on my notes:
https://x.com/nrehiew_/status/2062013300196700395
this was an insanely good read, i think this is the most detailed report i’ve read at this scale in some aspects. i really hope MAI continues releasing those tech reports, thanks a lot to the team for this gift 🥹
https://x.com/eliebakouch/status/2062004670017486912
Today, Baseten and @MicrosoftAI are excited to announce that MAI-Thinking-1 is coming to Baseten. MAI-Thinking-1 is a model you can fine-tune without giving your data to the lab. Key characteristics include: → Clean data lineage, with zero distillation from third-party models
https://x.com/baseten/status/2061878701823066431
We’re excited to work with @Baseten to make MAI-Thinking-1 available to developers and enterprises.
https://x.com/MicrosoftAI/status/2061923309344756043
WOW microsoft new “”MAI Thinking 1″” model comes with a 109 page tech report that looks REALLY detailed, this is amazing
https://x.com/eliebakouch/status/2061877335960281459
Microsoft has released MAI-Transcribe-1.5: an exceptionally fast speech transcription model at a speed factor of ~276x, while still achieving 2.4% on AA-WER (#3), leading the accuracy-speed Pareto frontier MAI-Transcribe-1.5 is Microsoft AI (MAI)’s latest speech transcription
https://x.com/ArtificialAnlys/status/2061878491860324402
Microsoft introduces MAI-Thinking-1 It’s a 1T@35B parameter model pre-trained on 30T tokens with a maximum context length of 256k tokens using 8192 GB200 GPUs. Based on benchmarks it seems to be around GLM-5 level. Microsoft also released a comprehensive 109 pages tech-report:
https://x.com/scaling01/status/2061889624847343825
microsoft used gepa / dspy to tune the LLM judge prompt for quality scoring. @lateinteraction stays winning. from the mai-thinking-1 report
https://x.com/bj2rn/status/2061941109828301241
Today we’re announcing MAI-Thinking-1 with Microsoft and it will be available on Baseten soon. Microsoft built something genuinely different here: a commercial-grade thinking model trained on clean data with no distillation from third-party models and designed to be fine-tuned
https://x.com/tuhinone/status/2061879239817969756
big congrats to the microsoft AI team on MAI-Thinking-1! this is the kind of thoughtful post-training the field needs more of – focused on what actually matters to users excited to see a new frontier model in the race 😎
https://x.com/echen/status/2061907282607100075
Introducing MAI-Code-1-Flash A new coding model from Microsoft for fast, efficient assistance in everyday workflows Rolling out to @code developers in model picker and Auto now!
https://x.com/pierceboggan/status/2061877165810131297
It is difficult to know how good MAI-Thinking-1 is from the scores alone (like weirdly low GPQA & Terminal Bench 2.0) But Microsoft makes it really hard to try its models upon release (a general issue with many Microsoft AI products), so I dunno. Stats below Meta Spark, though.
https://x.com/emollick/status/2061907785768489127
MAI-Image-2.5 has officially released from @MicrosoftAI landing at #2 in the Image Edit Arena (Single-Image-Edit) with a score of 1401 and advances the Pareto frontier! This puts the model +10 pts over Nano Banana 2, Grok Imagine Image Quality and ChatGPT-Image-Latest-High
https://x.com/arena/status/2061887242579382660
MAI-Image-2.5 ranks #2 in the Image Edit Arena and advances the Pareto frontier. That means: at its price tier, no model scores higher on Arena. Congrats again to @MicrosoftAI on this release!
https://x.com/arena/status/2061894541888962712
Microsoft AI has announced their very own reasoning model, MAI-Thinking-1, they have a detailed tech report too! I really appreciate they’ve reported health evals: HealthBench Professional and MedXpertQA. These are both very solid benchmark tasks that I recommend people use.
https://x.com/iScienceLuvr/status/2061926066453962952
microsoft published all the details of training their trillion parameter model:
https://x.com/ethanCaballero/status/2061920873297088723
MiniMax just dropped M3! It hits 59% on SWE-Bench Pro, edging out GPT-5.5 (58.6%) and beating Gemini 3.1 Pro (54.2%). Trails Opus 4.7 on coding, but leads it on autonomous browsing at 83.5% on BrowseComp. First open model to pack frontier coding, a 1M-token context, and native
https://x.com/kimmonismus/status/2061473350766170420
MiniMax M3 is now the leading open model on the Next.js agent evaluations (https://t.co/SnZ54XoRWV). Right behind Opus & GPT5, but 10× cheaper (And 20× cheaper right now on ▲ AI Gateway!)
https://x.com/rauchg/status/2061593874498531707
A search framework for stronger LLM reasoning – Bidirectional Evolutionary Search, or BES by @Harvard and @MIT It combines: – forward search to create and improve candidate solutions – backward search to breaks the task into checkable sub-goals + BES can recombine parts of
https://x.com/TheTuringPost/status/2060194173505155358
New research project maps the mathematical architecture of consciousness
https://www.news-medical.net/news/20260603/New-research-project-maps-the-mathematical-architecture-of-consciousness.aspx
[2605.31268] Mellum2 Technical Report
https://arxiv.org/abs/2605.31268
@lateinteraction it was my idea 🙂 Using GEPA is a very natural workflow for creating LLM programs. The iteration speed is very quick, and it easily allows researchers to bias the optimization with some priors (usually derived from just looking at the data). Thanks a lot for the great tool!
https://x.com/harold_matmul/status/2062040746027315714
@soldni to end off, despite all the good stuff in this paper, it really seems like the value of synth data can’t be understated. look at the sheer agentic performance deltas here
https://x.com/stochasticchasm/status/2061961874879783376
🎉 Congrats to @JetBrains on Mellum2-12B-A2.5B-Thinking, an open-source 12B MoE that activates just 2.5B params, handling both natural language and code with a 128K context. Mellum2 runs natively in vLLM from day 0, with reasoning parser and tool calling for agentic workflows.
https://x.com/vllm_project/status/2061621691995005301#m
🚀 MiniMax M3: Aiming for the Stars Zhihu contributor toyama nao shares an early evaluation of MiniMax’s new M3 multimodal model. 🔮 TL;DR Back in April, GLM-5.1 pulled decisively ahead of MiniMax M2.7 and took the domestic coding crown. Two months later, MiniMax responds with
https://x.com/ZhihuFrontier/status/2061493401019957337
1. Video control + gaming + M3 2. Open weights + massive context ++ strong coding 3. Canceling my weekend plans now
https://x.com/MiniMax_AI/status/2061425142795034794
6 guides to understand how LLMs actually work – What is a Token? – How token taxonomy affects your bill – Embeddings (including RoPE) – Agentic Vector Databases – Attention: Mechanism, QKV, and KV Cache – From tokens to answers: What actually happens during LLM inference Links
https://x.com/TheTuringPost/status/2059802853607452794
a 109 pages dear sweet jesus
https://x.com/yacinelearning/status/2061914159235617056
A must-read survey to refresh math and gen AI basics → The Little Book of Generative AI Foundations: An Intuitive Mathematical Primer It shows a clear walkthrough of how gen AI learns to understand, model, and create complex data, covering: – Latent algebra foundations: PCA,
https://x.com/TheTuringPost/status/2061243486419185879
Accuracy up there with the best open weight models, but 2-6X faster, thanks to using very little full attention and relying mostly on SSM, as well as LatentMoE. Pretrained in NVFP4. We detail the pretraining process including two divergences we ran into, along with much more
https://x.com/ctnzr/status/2062515418884149451
Crusoe Serverless Fine-Tuning | Private Preview
https://www.crusoe.ai/contact-sales/serverless-preview
Dynamic model routing products have largely been snake oil so far. We’ve seen many come and go since 2022. The story of model routing has a simple, legible quality that magnetizes capital. @Alfred_Lin’s “Beware of Simple Narratives” speaks to the danger of this:
https://x.com/scottastevenson/status/2062042036774314107
everybody’s building the same thing. the IDE of the future replaces code with prompts, file viewer with thread viewer, and turbo bundles the building lifecycles (plan, design, build, deploy, monitor, fix, etc.) but who’s gonna figure out collaboration? do you make every
https://x.com/gakonst/status/2062116487708512355
for tokens per parameter (TPP), they mention it varies by ablation. ablations run at 100/200 TPP which is around “”chinchilla optimal””. chinchilla for dense is ~20 TPP, so a ~5-10x factor from their MoE setup? interesting
https://x.com/eliebakouch/status/2061975730414633043
fully agree with this take on the problems with model routing there’s more: 5. constant model routing can mean lots of cache writes! that quickly eats up any and all cost savings you might otherwise see 6. harness-model-prompt fit isn’t always straightforward
https://x.com/fabianstelzer/status/2062051511484465351
Here’s the technical writeup on how the measurement works:
https://x.com/cognition/status/2062597246001324518
Hey, its our paper!
https://x.com/emollick/status/2062191515925938266
Hillclimbing from scratch is for sure suffering but we learned a ton by going through the pain. So proud of our team 💫 Check out our technical paper:
https://x.com/MinjiYoon90/status/2062058684730245376
If MiniMax flops generally as hard as it did in my tests, I hope the market reacts accordingly. They’re aurafarming too much, raking in too much for their achievements to date, and I don’t like it. Prove me wrong.
https://x.com/teortaxesTex/status/2061432151183171702
if you think model routing is going to work for enterprise use cases in production, you really don’t know anything about production AI. shit may break going from one model to another and only you know how to evaluate it. not some random router you don’t even own.
https://x.com/glennko/status/2061896887699964171
Introducing Wall Attention. Diagonal forget gates enable RoPE-free attention with exceptional length generalization. Wall outperforms the dominant method RoPE and sophisticated data-dependent methods like Forgetting Attention (FoX). We trained models with Wall on 4k sequence
https://x.com/tilderesearch/status/2061839600562409581
M3 live on @novita_labs 🔥 it’s time to build (50% off the first week 👀)
https://x.com/MiniMax_AI/status/2061398427121201648
MiniMax M3’s frontend capabilities are pretty nice very strong model for the price. not lazy, thinks through the task (thinks a lot), and doesn’t just take the shortest path M3 can reason between multiple design choices better than i expected with the right skills around it,
https://x.com/lostinlatencyX/status/2061409696649548165
MiniMax promises M3 weights after 1M launch
https://www.implicator.ai/minimax-promises-m3-weights-after-1m-context-model-launch/
MiniMax-M3 combines 1M context, native multimodality, and MiniMax Sparse Attention. The next layer is serving it efficiently: KV-block-major sparse attention, paged MSA decode, optimized index scoring, and multimodal preprocessing before the GPU worker. Together’s Inference and
https://x.com/togethercompute/status/2061895336486949109
Model routing is an important thing Controversial idea: the frontier labs will want their AI harness to be the moat, but ultimately the best case for consumers is that model capabilities flatten and commodify Preview of the AI Harness Wars of 2027
https://x.com/garrytan/status/2061878212213572083
quite a gold mine, so much info
https://x.com/stochasticchasm/status/2061879506139557979
Simplicity + Performance. Impressed by the work of the team to remove all the encoders from a multimodal Transformer and keep its quality.
https://x.com/armandjoulin/status/2062206784647967075
some pretty crazy RL graphs, and very interesting decision here to start RL from a checkpoint with no reasoning exposure
https://x.com/stochasticchasm/status/2061879070141677615
the “”loss”” definition is VERY important, the scaling ladder heavily relies on this. it’s a NLL private set (negative log likelihood) with: 50% code 17.5% STEM 17.5% Math 10% General knowledge 5% Multilingual they then use this target NLL and normalize it with an in-house model.
https://x.com/eliebakouch/status/2061976608265880004
The everything apps still look a lot like hybrids between chatbots and IDEs, rather than something built for general knowledge work. Too much assuming linearity & that final outputs are the only goal, too little connection to research, not enough chances to steer or select, etc.
https://x.com/emollick/status/2061931631124914452
the rule to promote a new architecture is based on this scaling ladder. they have this Efficiency Gain (EG) metric which basically quantifies “”to reach the loss our candidate got, how much more compute would the baseline have needed?”” “”compute”” here can mean flops or time, but
https://x.com/eliebakouch/status/2061976230933496176
There is a lot being written about the stylistic tells of AI writing (em-dashes, etc.) but this paper looks at AI narrative tells Fascinating differences between AI & human narrative, and asking AI to write in different styles doesn’t do much to change it
https://x.com/emollick/status/2059851903089930685
This is probably my longest paper thread haha but this is one of my favourite tech reports ever. I highly encourage everyone to read it. The entire thing is 109 pages and took me quite a bit of time to go through but it could really serve as an ~updated textbook for LLM training
https://x.com/nrehiew_/status/2062023547690828141
TinyFish Bigset turns text prompts into live datasets
https://www.testingcatalog.com/tinyfish-bigset-turns-text-prompts-into-live-datasets-from-web/
Today we’re releasing Mellum2: our first “”serious”” LLM. This is a 12B A2.5B MoE LLM pre-trained on ~11T tokens and post-trained with RLVR. I’m proud to be leading the team that was working on it for the last 6 months. We release base/SFT/RL checkpoints along with a tech
https://x.com/nv_pavlichenko/status/2061438808290172935
We collaborated with Hugging Face, llama.cpp, Ollama, VLLM, SGLang, Unsloth, MLX, LM Studio, and the rest of the ecosystem to land day 0 support. Enjoy! Read our developer guide:
https://x.com/osanseviero/status/2062205176597889220
We pre-train LLMs on the whole of the internet. You might think this explains how they learn so many emergent capabilities: the knowledge is implicit in the training data. But in fact models can do things that were never demonstrated anywhere in training! @svlevine argues that
https://x.com/dwarkesh_sp/status/2060798017679319527
We wrapped a live session on M3 yesterday with the @togethercompute team & our researchers @zpysky1125 and @HaohaiSun A few highlights 🧵 1. MSA (MiniMax Sparse Attention) is the star ⭐️. Unlike CSA/HCA, which compress the KV cache, MSA keeps the real, uncompressed KV and
https://x.com/MiniMax_AI/status/2061944204604101020
// Reusable Context Engineering // Context bloat quietly kills long-horizon runs, but you can fix it from the outside without fine-tuning the underlying agent. (bookmark this) Context management is usually baked into an agent’s own prompt or weights, which does not transfer
https://x.com/dair_ai/status/2061455253325971789
Ken Rogoff, former IMF chief economist, points out that countries can go bankrupt even with good growth. Debt crises are really about politics: whether leaders are willing and able to manage tax, spending, and inflation. Growth gives leaders more flexibility, but doesn’t
https://x.com/dwarkesh_sp/status/2062248226179485864
Reconstructing software engineering around AI is going to take work (even as the ability of AI to code increases at a rapid rate). Organizations are ideally spending tokens for two things: 1) building stuff 2) experiments to figure out best practices (which involves failure)
https://x.com/emollick/status/2060357604044358108
A note from Jensen Huang about how they were building the new Surface RTX Pro developer machine
https://x.com/TheTuringPost/status/2061857391302541338
Open and closed models are on different exponentials
https://www.interconnects.ai/p/open-and-closed-models-are-on-different
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