elvis(@omarsar0)

LLMs were just the beginning. Bullish on world models, and I think they are the next frontier in AI....

elvis (@omarsar0) — Mon, 19 Oct 2026 18:54:22 GMT

LLMs were just the beginning. Bullish on world models, and I think they are the next frontier in AI.

Amazing to see a dedicated world model for engineering this early. SKY-001 against GPT-6 Astra matches accuracy on 3D construction at about 150x lower cost per sample. On BIM, it runs over 100x faster.

Your browser does not support the video tag.

Sam Pasupalak@spisallyouneed

Introducing SKY-001, the first Engineering World Model.
On CAD and BIM tasks, it beats GPT-6 Astra and every frontier model we tested:

-> 51-125x faster
- > 150-210x cheaper
-> Higher IOU than all frontier models

SKY-001 lets you dream a physical object before you build it.

18 months of work. Built by the team out of Maluuba's deep learning lab (acq. by @MSRResearch).

The first ‘AI General Engineer’ will be the backbone for Autonomous Manufacturing.

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Very excited for this. Ed’s point about compute access in the era of agents is spot on. Plus, AI i...

elvis (@omarsar0) — Mon, 19 Oct 2026 17:17:28 GMT

Very excited for this.

Ed’s point about compute access in the era of agents is spot on.

Plus, AI infrastructure needs changes to meet the demands of agentic engineering.

Agents may need GPUs for inference and CPUs to run code in the same workflow (e.g., co-evolution of model and harnesses). I want rental options that fit those changing needs.

Follow @edsgoode and @shadeformai this week for cool launches around this.

Ed Goode@edsgoode

The compute market has been a wild ride in 2026.

Prices have skyrocketed, minimum contract durations have tripled, and near term capacity is few and far between.

At the same time, agents are becoming a primary user of compute themselves, and not just GPU compute.

It’s clear that the existing rental models and platforms across the industry no longer fit the reality we see today.

That’s why this week we’re announcing six new features for the Shadeform platform 👇️

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Follow for more here: https://t.co/UB4MM6lwvo

elvis (@omarsar0) — Mon, 19 Oct 2026 17:17:28 GMT

Follow for more here: shadeform.ai/launch-week-20…

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Banger paper from Google DeepMind on open-ended math discovery with agents. This is one of the earl...

elvis (@omarsar0) — Mon, 19 Oct 2026 16:55:02 GMT

Banger paper from Google DeepMind on open-ended math discovery with agents.

This is one of the early, exciting works I have seen on autonomous AI-powered discoveries.

(bookmark it)

A Gemini-based agent found and formally verified new acceleration rules for eleven unresolved Turing machines in BB(6), the current frontier of the Busy Beaver problem.

Busy Beaver is uncomputable, so no fixed list of problems exists. The agent has to pick machines, spot patterns in their execution traces, and propose compact rules that summarize many steps at once.

Each proposed rule then goes through informal reasoning and a Lean proof. Only proved rules are kept, so the result does not depend on trusting the model's reasoning.

The authors provisionally call these machines Cryptids. Their runs from a blank tape now have exact, compact descriptions, but whether they halt is still open.

This is a good example of an agent producing new, formally checked mathematical results on problems it chose itself.

arxiv.org/abs/2610.22603

Chat with Paper: academy.dair.ai/papers/at-the-…

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Viggle's JST-2 comes with a PINOC MCP server, so one prompt to Claude Code can make the characters, ...

elvis (@omarsar0) — Mon, 19 Oct 2026 16:07:13 GMT

Viggle's JST-2 comes with a PINOC MCP server, so one prompt to Claude Code can make the characters, animate them from text, and put the scene together. Characters and motion share one skeleton, so there's no retargeting step for the agent to choke on halfway through. Would love to see someone build a small game start to finish this way.

Your browser does not support the video tag.

ViggleAI@ViggleAI

Today we are releasing JST-2, the world's first full-stack physics-native world foundation model.  

Many world models learn in pixel/video space. We went back to first principles and built the entire model stack in 3D.  

Generate a 3D scene in seconds and expand it in real time. Create characters with motion built in, all through one general architecture.

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I have been doing some early testing and will share other cool applications in a longer post soon. T...

elvis (@omarsar0) — Mon, 19 Oct 2026 14:18:11 GMT

I have been doing some early testing and will share other cool applications in a longer post soon. Try Drex Max here: console.nace.ai

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Decision models are going multimodal. Drex Max looks really strong. It can make fast decisions stra...

elvis (@omarsar0) — Mon, 19 Oct 2026 14:18:10 GMT

Decision models are going multimodal.

Drex Max looks really strong. It can make fast decisions straight from screenshots, screencasts, scanned documents, scientific charts, and photos.

This model is a really good fit for computer-use and browser-use agents, where every step starts with reading the screen.

Nace reports 88.3% on Mind2Web (outperforms Jev 1.14).

Drex Max also scores 67.6 on the Decision Index (Jev 1.14: 60.70).

Your browser does not support the video tag.

Nace AI@NaceAI

Drex Max is here: our multimodal decision model
text, images, and video
34 ms/decision, under 1s at 262K tokens
$0.06 / 1M input tokens
available today at nace.ai/drex

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Apply to the Nebius AI Builder Program here https://t.co/wUJBCeqHcu

elvis (@omarsar0) — Mon, 19 Oct 2026 13:48:56 GMT

Apply to the Nebius AI Builder Program here dev.nebius.com/builders

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No better time to learn how to build with agents and open models. I encourage students to apply con...

elvis (@omarsar0) — Mon, 19 Oct 2026 13:46:05 GMT

No better time to learn how to build with agents and open models.

I encourage students to apply context engineering techniques, then build their own domain-specific harnesses. And don't forget to build evals along the way.

If you want a place to start, the Nebius AI Builder Program is expanding to Europe, with $10M across compute grants, capital, and community.

Nebius@nebiusai

The AI Builder Program is expanding across Europe with $10M in total support across compute grants, capital, and community programs.

Are you building open models, developer tools, or applications on top of them? The program helps builders go from experimentation to scale-up through:

- Compute grants for selected projects in areas such as open models, coding agents, post-training, and evals
- VC connections to help turn open source projects into companies
- Hackathons and demo days with communities including @AITinkerers, @techeurope_ , and @esra_robotics to build, connect, and share your work

We’re kicking off the European expansion this week at #NVIDIAGTC Berlin with @NVIDIAAI, @Accel, and a network of European VCs.

Building AI in Europe? Details below. 👇

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Generally, good advice here. Systems thinking is at the top of my list too. That’s how you interact ...

elvis (@omarsar0) — Mon, 19 Oct 2026 12:52:23 GMT

Generally, good advice here. Systems thinking is at the top of my list too. That’s how you interact and build effectively with agents today, by thinking of the subparts of the system.

I would add understanding how to measure outcomes and build evals. It comes with experience but it’s something you want to apply early on. If you are not objectively measuring what you build it’s hard to know if you are making real progress. Use agents to help you get started but always look at the data and traces.

Thorsten Ball@thorstenball

I often get emails from junior engineers asking me what to do.

Here's what I just wrote to one of them

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Learn how to build evals, folks! It's one of the pillars of the intelligence stack. Agents are hu...

elvis (@omarsar0) — Mon, 19 Oct 2026 00:23:47 GMT

Learn how to build evals, folks!

It's one of the pillars of the intelligence stack. 

Agents are hungry for data and can get it in many ways, but you can't build anything useful in the real world without proper evaluation. Evals make agents useful and let you build truly differentiated products. 

Invest heavily here. Or reach out if you need help. We are also building evals to measure the frontier @dair_ai.

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Nice paper on scaling data environments from scratch. They built an engine that creates data-analys...

elvis (@omarsar0) — Sun, 18 Oct 2026 21:45:02 GMT

Nice paper on scaling data environments from scratch.

They built an engine that creates data-analysis environments from scratch. They apply RL, which adds 20.9 points on DABstep-Hard.

Data World first writes down the mechanisms that generate a dataset, then produces the files, tasks, and grading rubrics. Because the generating process is known, open-ended findings can be checked, and independent audits confirm each answer can be recovered from the files.

Training Qwen3.6-35B-A3B with RL on 2,135 of these tasks, with no distilled trajectories, also adds 14.28 points on DAComp and 12.13 on JobBench.

The benchmark finding is just as useful. Ten frontier models compute the right numbers more often than they draw the right conclusion from them.

arxiv.org/abs/2610.21656

Chat with Paper: academy.dair.ai/papers/data-wo…

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// Latent Semantic Memory for LLMs // This paper presents some really interesting findings about ag...

elvis (@omarsar0) — Sun, 18 Oct 2026 17:59:02 GMT

// Latent Semantic Memory for LLMs //

This paper presents some really interesting findings about agent memory.

They find that sixteen memory tokens beat full-context inference on FactKG while using 108x fewer tokens for the source documents.

Approach:

Documents are encoded once into reusable embeddings. For each query, a learned module gathers the relevant information from the whole collection and writes it into 16 tokens in the input space of a frozen LLM.

Results:

It beats Mem0, MemoRAG, and other baselines on five of eight benchmarks. On FactKG, it scores 84.33% against 80.43% with the full context.

It also works as agent memory. In TextWorld, using the agent's own history as the collection raises success from 18.6% to 24.2%, and the agent revisits fewer rooms.

It also seems that it could help frozen LLMs accumulate and use knowledge effectively across long-context interactions. This is applicable to building continually learning AI systems.

arxiv.org/abs/2610.21375

Chat with Paper: academy.dair.ai/papers/memoril…

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Great application of small language models from Salesforce and colleagues. AI-scientist pipelines a...

elvis (@omarsar0) — Sat, 17 Oct 2026 22:04:02 GMT

Great application of small language models from Salesforce and colleagues.

AI-scientist pipelines are well known to struggle to propose novel and diverse research ideas.

This team trains an 8B reasoning model to write several research ideas in one generation from a set of source papers.

The reward scores the whole set, weighting each idea's quality by how far it sits from the ideas written before it. The model learns to move each new idea away from the earlier ones.

On their contamination-free benchmark, the 8B model scores higher on creative utility, a measure that combines quality and diversity, than Claude Opus 4.8 and GPT-6 Astra.

arxiv.org/abs/2610.22324

Chat with Paper: academy.dair.ai/papers/trainin…

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Mind-blowing to see these Pareto improvements in performance and cost. More than anything, it's cr...

elvis (@omarsar0) — Sat, 17 Oct 2026 19:50:46 GMT

Mind-blowing to see these Pareto improvements in performance and cost. 

More than anything, it's crazy how quickly these improvements are landing across providers. 

This matters a lot to builders.

DeepSeek@deepseek_ai

🚀 DeepSeek-V4.1-Flash-1017 is here!

Stronger benchmark performance, faster task completion, and shorter outputs than the previous version—a Pareto improvement in performance and cost. Now live on our website, app, and API.

🛠️ Optimized for @DeepSeekHarness, Tencent CodeBuddy, WorkBuddy, and more. Upgrade to DeepSeek Harness v0.2.1-rc.1 or later for the best experience with the new model.

1/4

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Recommended paper integrating decision models like Jev into harnesses.

elvis (@omarsar0) — Sat, 17 Oct 2026 16:35:57 GMT

Recommended paper integrating decision models like Jev into harnesses.

DAIR.AI@dair_ai

Must-read paper integrating decision models like Jev into harnesses.

They distilled an open 4B decision model, mostly from Jev's answers on the agent's own states, that beats Jev inside that agent and is much cheaper to run.

Their harness, JevAgentHarness, sends each routine choice to the decision model and uses its answer only when the confidence clears a threshold fitted on the agent's own trajectories. Below the threshold, the LLM decides as usual. With the fitted threshold, tool and argument cascades cut cost by 14.2% without changing the outcome of any of 500 tasks. A fixed threshold of 0.5 lost 21.5 points.

They also train AgentDecision-4B on states the agent reaches after its own mistakes, with Jev's answers as labels. On tasks unlike its training data, the agent solved 91.5% with it and 82.0% with Jev. On familiar tasks, 4B models trained this way made agents 81.1 to 89.0% cheaper and up to 2.5 times faster while staying within 3 points of success.

Paper: academy.dair.ai/papers/jevagen…

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Banger paper from Tencent on recursive self-improvement for terminal agents. (bookmark it) This is...

elvis (@omarsar0) — Sat, 17 Oct 2026 16:00:19 GMT

Banger paper from Tencent on recursive self-improvement for terminal agents.

(bookmark it)

This is a great read if you are tracking research on training terminal or coding agents with RL

Starting from Qwen3.8-27B, they alternate two phases over five stages. One phase fine-tunes on rewritten versions of trajectories that passed the verifier. The other runs PPO with a critic inherited from the previous stage.

The four-suite Terminal-Bench average goes from 20.68% to 50.58%, with 86.52% on Terminal-Bench 2.1 and 79.00% on Terminal-Bench Hard.

The reward audit is just as useful. Under a dense progress reward, failed runs collected 50.09% of the reward, and 48 failures passed every executed check but still missed the final test. Separating strict success from partial credit cut that share to 28.72%.

arxiv.org/abs/2610.22192

Chat with Paper: academy.dair.ai/papers/termina…

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Real talk. PRs suck in the age of coding agents. There must be a better way. I agree with this post,...

elvis (@omarsar0) — Sat, 17 Oct 2026 02:02:12 GMT

Real talk. PRs suck in the age of coding agents. There must be a better way. I agree with this post, agents are so capable now. Let them do the heavy lifting. They can generate (on demand) the right personalised views and artifacts that make things move a lot faster.

Thorsten Ball@thorstenball

Why are you still using pull requests?

Is it for code review?

If so, do you really think your or your colleagues catch bugs and mistakes that an Opus 5.5 or Fable or Astra won't find? You can literally spawn all three of them and have them check four times each.

(Also, don't tell me you don't have a "stamp plz" Slack channel or group chat somewhere.)

Is it for knowledge sharing?

Surely there's better ways for that now than the PR, no? We have models that can generate explainer videos, presentations, podcasts, games, quizzes, walkthroughs, ... for any piece of code it can reach. 

Is CI the reason?

Well ackshually, CI means continuous integration and its original meaning is the *opposite* of having long-lived branches and PRs.

But, leaving that aside, your agents are hopefully already running the tests before they push something, and hopefully they can run as long as they want and don't block you so they can run the whole test suite and generate "irrefutable proof" and end-to-end demo videos and so on... And so they run the tests and confirm and then you push to a branch and everything runs again and then you merge and then it runs again? That doesn't make sense, does it?

Maybe you work in open source and you accept contributions, in which case I can't speak to the value of pull requests. It's been 2 years since my last OSS contribution. But from what I heard, the time of the PR is coming to an end in OSS too?

I honestly don't get why people still stick to PRs.

With agents you can now move 10x faster, you can finally do real continuous integration, and get more confidence in the changes the agent made.

Don't slow yourself down by having all the changes pile up in a PR.

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Surprised that so few people are talking about this. Persistence has become a core capability of re...

elvis (@omarsar0) — Fri, 16 Oct 2026 20:38:23 GMT

Surprised that so few people are talking about this.

Persistence has become a core capability of recent models (Opus 5.5, GPT 6 Astra, Gemini 4 Argon,...). 

I was just reading through the Gemini 4 Argon Minecraft paper details and noticed that no roles were assigned to the agents.

Somehow the four agents completed the goal after 27 hours, 2,783 programs, and 605 deaths. They recovered items, revised strategies, and shared lessons. I think this is a good example of persistence helping them eventually succeed.

Some more thoughts for discussion:

So this is not just about hooking an agent up to an external memory system.

It's also not about loop-prompting it with /goal, although that is one of the most effective techniques right now.

This is about the model's native ability to choose what to persist, and how and when to use it.

It unlocks long-horizon capabilities, as agents often execute more complex tasks more effectively through persistence.

So it's less that the models got more intelligent and more that they got better at coordinating and planning for more complex tasks. 

We are in the early days of this. But this is an important capability to optimize to really unlock AI for scientific discovery. 

This is also important for building more personalized agents with identities and roles that can track and follow human preferences well and learn continually over time. 

We also don't entirely know how to quantify and measure it as a field. Reward hacking combined with persistence can also enable weird, unsafe agent behaviors. 

Just a few quick thoughts, and I hope we can start deeper conversations about this. 

I am tracking this space closely and will be sharing research on the topic as it surfaces.

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Pay attention to this new wave of VLMs. Glimpse is the first model from @ElorianAI, the brains that...

elvis (@omarsar0) — Fri, 16 Oct 2026 17:22:50 GMT

Pay attention to this new wave of VLMs.

Glimpse is the first model from @ElorianAI, the brains that led Google's Gemini and Apple's MM-1.

Quick test: 10 game boards from an overhead camera. Same prompt, same image, two models, hand-checked.

Glimpse: 8/10 boards fully correct.
Gemini 3.8 Flash: 5/10.

This is what data labeling looks like. Getting the whole frame right, not just most of it, turns raw robot footage into training data.

This is powerful Visual Thinking, not just visual recognition translated into text.

Your browser does not support the video tag.

Andrew M. Dai@AndrewDai

Spent years watching models ace a single image and completely whiff on anything that moves or changes angle. Today we’re fixing that.

Try Glimpse now, our first visual thinking model with competitive frontier performance compared to Astra on key public visual reasoning benchmarks.

Your browser does not support the video tag.

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I think robots that live alongside people will need a different kind of intelligence than robots bui...

elvis (@omarsar0) — Fri, 16 Oct 2026 16:12:32 GMT

I think robots that live alongside people will need a different kind of intelligence than robots built for factories. 

Empathy and personalization matter here. 

Hugo is a human companion robot from @theermann and Farbod Farshidian, who spent 15+ years teaching robots to run, jump, and work at Boston Dynamics and the RAI Institute.

Curious how customizable this could be. I see great educational applications for this.

theermann@theermann

Introducing Hugo: the first human companion robot.

At Boston Dynamics, I was Chief Strategy Officer, bringing robots into the real world.

My co-founder, Farbod Farshidian, led research at the RAI Institute.

Together, 15+ years teaching robots to run, jump, and work.

So we asked: what if robots could connect with people?

Hugo runs on the first Vision-Language-Expression (VLE) model.

He links what he sees and hears to movement, expression, and gesture.

He notices you, reads the room, and reacts in real time.

And he's only the first.

Dynamic Creatures is @BostonDynamics' official entertainment and hospitality partner.

We're building a cast of characters with film studios, resorts, and theme parks.

dynamiccreatures.com

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Banger paper from Google DeepMind on long-horizon multi-agent coding. (bookmark it) They report th...

elvis (@omarsar0) — Fri, 16 Oct 2026 13:53:01 GMT

Banger paper from Google DeepMind on long-horizon multi-agent coding.

(bookmark it)

They report that, to the best of their knowledge, this is the first end-to-end autonomous completion of Minecraft by AI agents.

They report that four Gemini 4 Argon agents beat Minecraft end-to-end. They defeated the Ender Dragon in 27 hours and 14 minutes with no human intervention.

Each agent controlled one player through the standard Mineflayer library. No one assigned roles, wrote game logic, or scouted a seed in advance. The agents planned, wrote, and debugged every program themselves, and coordinated through in-game chat and a shared memory.

Over the run, they wrote 2,783 programs and died 605 times. They split the work, shared what they learned, and recovered each other's items after deaths.

If you build agents for long software or enterprise tasks, this is a useful case study. The team had to keep one plan consistent across thousands of dependent steps for more than a day.

arxiv.org/abs/2610.22319

Chat with Paper: academy.dair.ai/papers/a-team-…

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It is pay-as-you-go, so you can test it on a small list. Try it here https://t.co/Q5Zrez7A6e

elvis (@omarsar0) — Fri, 16 Oct 2026 13:37:25 GMT

It is pay-as-you-go, so you can test it on a small list.

Try it here app.aisdr.com/signup

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AI agents are useful in outbound when they handle the entire email workflow. Ami from AiSDR researc...

elvis (@omarsar0) — Fri, 16 Oct 2026 13:37:24 GMT

AI agents are useful in outbound when they handle the entire email workflow.

Ami from AiSDR researches each prospect, writes a personalized email, sends it, follows up, and handles replies. It also manages the email infrastructure and warm-up.

AiSDR now charges $1.50 per researched prospect with unlimited touchpoints.

Yuriy Zaremba@yz_aisdr

Today AiSDR is disrupting pricing for outbound sales.

$1.5 per person prospected, unlimited touchpoints, EVERYTHING included. No minimum.

We will take care of email infra, warmup, leads (2B+ contacts, including off-LinkedIn), enrichment (waterfall of 10+ providers), AI-researched, unique multi-channel sequence to each prospect, AI reply-classification and reply handling, everything syncs back to your CRM and tracks influenced and closed won pipeline.

Ami AI, trained on 20k+ qualified meetings booked across 27 industries to help you ideate, monitor, improve and run your campaigns.

All the latest and greatest in GTM tech on one platform, starting at $1.5. 

There is literally no alternative to this offer.

- Have an offer and an audience to test for 100 leads - AiSDR is your place to go
- Want to test outbound, but buying 5 tools and managing them feels like a pain - AiSDR
- Planning to go to a conference and need to book few meetings - AiSDR
- Just came back from a conference and have a 200 leads to follow-up with - AiSDR

Any kind of outreach via email and LinkedIn - we've got you.

Fully autonomous with the best GTM AI agent you can buy in the market.

Outbound on demand, priced like ads.

Try it today at aisdr.com.

Your browser does not support the video tag.

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Brilliant paper on scaling research agents from UC Berkeley. (bookmark it) They propose an unexpec...

elvis (@omarsar0) — Fri, 16 Oct 2026 01:50:02 GMT

Brilliant paper on scaling research agents from UC Berkeley.

(bookmark it)

They propose an unexpected and interesting way to scale automated research with agents.

It's common to see research agents keep their full history and iterate on it to solve a task. After a while, each agent anchors on its own past attempts, makes smaller edits, and extra budget stops improving things.

So this work induces forgetting in the agents.

Periodically, they discard each agent's session and the candidate history, and then restart from the best candidate found so far.

Across heuristic programming, kernel optimization, and model training tasks, this gives up to 15% over parallel agents and up to 39% over a single agent with the same budget.

arxiv.org/abs/2610.20563

Chat with Paper: academy.dair.ai/papers/scaling…

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@dair_ai The main observation from my own experiments is whether there are any efficiency gains. I d...

elvis (@omarsar0) — Thu, 15 Oct 2026 21:33:08 GMT

@dair_ai The main observation from my own experiments is whether there are any efficiency gains. I don't question the performance.

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LLM-based agents love structured data. I've seen this work really well, but mostly in complex code...

elvis (@omarsar0) — Thu, 15 Oct 2026 21:06:13 GMT

LLM-based agents love structured data. 

I've seen this work really well, but mostly in complex codebases. But I think context graphs have broader applications, as shown in this Harvey report on legal knowledge tasks. 

We are working on this at @dair_ai for research-related tasks. More to share soon.

Harvey@harvey

We built context graphs to help agents work through long-horizon legal tasks.

Starting with Legal Agent Bench tasks in 11 categories like M&A diligence and contract negotiation, we had agents build context graphs of the underlying documents (without access to the eval tasks or grading rubrics).

Context graphs dramatically improved agent performance on LAB tasks:
- Firm Knowledge tasks: 65% → 80%
- Precedent lists tasks: 67% → 80%
- Matter counting tasks: 53% → 83%

They also boost agent efficiency: we found a 48% cost reduction for agents given access to a context graph to assist their work.

Full video below - more to come soon.

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Starting from a real photo and editing from there feels a lot more practical than prompting from scr...

elvis (@omarsar0) — Thu, 15 Oct 2026 20:32:58 GMT

Starting from a real photo and editing from there feels a lot more practical than prompting from scratch over and over. You can try it here elements.envato.com/lp/riffing/?ut…

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This is a cool product. In my experience, the best AI images start with a real photo. Prompting fro...

elvis (@omarsar0) — Thu, 15 Oct 2026 20:32:57 GMT

This is a cool product. In my experience, the best AI images start with a real photo.

Prompting from scratch usually gives me something generic. Editing a good photo keeps whatever made it work in the first place.

Nice to see @envato lean into this. You pick a photo from their library and edit it with AI from there.

Envato@envato

Pure AI: six fingers and a vibe.

Pure stock: the same photo your competitor used last week. 

So why choose between? Start with something real. Riff until it's right.

30M+ artist-created assets. Now editable with AI on Envato.

Your browser does not support the video tag.

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Damn! Looking at the results, Meta really cooked here! More progress on frontier open models is al...

elvis (@omarsar0) — Thu, 15 Oct 2026 18:24:58 GMT

Damn!

Looking at the results, Meta really cooked here!

More progress on frontier open models is always a big deal.

Great to see Meta going back to its roots. The only way to know this checkpoint's quality is to test it yourself.

AI at Meta@AIatMeta

Today we’re releasing Muse Spark Open, the most capable US open-weight model to date.

Built for coding and agentic work, it excels at long-horizon tasks, complex software engineering, and multi-step reasoning.

Key Model Specs:
→ 816B total / 42B active parameters
→ 1M token context window
→ Natively multimodal across text, image, video, audio

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Highly recommended read. This is the type of interpretability work that we need more of. "We can n...

elvis (@omarsar0) — Thu, 15 Oct 2026 17:51:01 GMT

Highly recommended read. This is the type of interpretability work that we need more of. 

"We can now understand most neurons in models up to trillions of parameters, and think the outlook for mechanistic interpretability may be far better than we thought."

Nick@nickcammarata

interpretability might be the most important scientific problem in the world right now, as I don't think we can solve alignment if we don't know what we're building. I think I have good news about it. I co-authored early work in mechanistic interpretability at OpenAI and came away pessimistic, because neural networks appeared to fold into arbitrary tangled knots. years later, I started openai's first large-scale automated interpretability project, finding about 2% of neurons legible with the primitive tools we had

with modern agentic tools and a much closer look, the picture looks totally different. the vast majority in models up to trillions of parameters are legible, and their circuits beautiful. many famous examples of tangled neurons turned out to be misunderstood. The roadblocks I expected mostly aren't there. I now think ambitious interpretability will be solved by the time we need it most, and possibly quite soon, as automated scientists keep improving. it may be a bitter lesson for interpretability: the simple thing, at absurd scale, works

already-clean.goodfire.com

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This is super cool. Texting a business should be as easy as texting a friend. At least that should b...

elvis (@omarsar0) — Thu, 15 Oct 2026 17:18:09 GMT

This is super cool. Texting a business should be as easy as texting a friend. At least that should be the standard.

@usebland turns any business website into a branded text line with an agent behind it. It pulls prices, hours, and inventory live from the site, and it can open the page in a real browser and text you a screenshot of what it found.

Text any website to +1 (415) 481-7537 to try it.

Bland@usebland

iMessage agents for your business in 30s. 

Send a link and Bland learns your business, plugs into your APIs, and creates a branded agent for you automatically. 

The agent can do things like Tapbacks (❤️,👍,❗), send embedded components, and even accept payments within a chat. 

It's live now. Text +1 (415) 481-7537.

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Build for agents, folks! Agents will do more and more work inside business software, so every app n...

elvis (@omarsar0) — Thu, 15 Oct 2026 17:03:08 GMT

Build for agents, folks!

Agents will do more and more work inside business software, so every app needs to work through APIs and permissions.

@ERPdotAI builds that in from the start. Every app you generate from a prompt is available through an API, and Proto, their agent, can build workflows and reports on top of it.

sphinx@protosphinx

For 25 years, Salesforce, SAP and enterprise software were built around one assumption: humans operate the software.

We’re changing that.

Introducing ERP•AI and Proto: business software built for agents, and an agent that operates business software.

Describe the system you need. ERP•AI builds it.

Then Proto takes over. It manages records, runs workflows and executes actions inside the software you just created.

This is Headless SaaS.

1: get ERP•AI
2: build your SaaS
3: post below

Best one wins a MacBook. First 100 decent entries get $100.

(a message from Steve Papa / founding team)

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Agents need a better interface. In my own agent orchestrator, I often render more actionable, valua...

elvis (@omarsar0) — Thu, 15 Oct 2026 16:56:04 GMT

Agents need a better interface.

In my own agent orchestrator, I often render more actionable, valuable outputs as small interfaces.

@openuidev makes this easy for any AI app. It is an open-source, model-agnostic library that streams a UI built from your own components.

I can easily see this becoming a standard way to interact with agents in the future. Go check it out!

Rabi Shanker Guha@rabi_guha

For 30 years, technology has moved in one direction: turning information into intuitive interfaces

Somehow, the smartest software we’ve ever built took us back to a textbox

We’ve spent the last 2 years at @openuidev trying to fix that. 6 months ago we open-sourced it: 10K+ GitHub stars, 6M+ downloads

ChatGPT shipped Intelligent UI and showed everyone what it looks like

Today we’re releasing OpenUI 1.0 bringing it to every other agent

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Recommended, must-read paper on building recursive, self-improving agent harnesses for open-world ta...

elvis (@omarsar0) — Thu, 15 Oct 2026 15:32:22 GMT

Recommended, must-read paper on building recursive, self-improving agent harnesses for open-world tasks.

DAIR.AI@dair_ai

Learn to build custom harnesses, folks!

Here is a highly recommended paper from Google on recursive agent harnessing.

Great insights if you work on evolving your harnesses.

It also offers an important discussion of recursive self-improvement and evolving harnesses for open-world tasks.

They focus on improving your agent harness at test time without a verifier. This allows interesting scaling properties.

Harness^2 proposes several edits to the harness for the current task, tests them against each other, and keeps the combination that works best. Lessons from those edits then update a second harness that the agent carries to future tasks.

Results:

It improved GLM-5 and Gemini 3.5 Flash in every configuration tested across WorkBuddy-Bench, JobBench, and LAB. Running five candidate edits in parallel adds another 2.9 to 3.9 points on WorkBuddy-Bench.

ReasoningBank, a memory baseline, fell up to 6.6 points below the base harness on coding tasks in the same comparison.

arxiv.org/abs/2610.14224

Chat with Paper: academy.dair.ai/papers/harness…

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Must-read paper from Sakana AI on orchestrating coding agents for scientific research. (bookmark it...

elvis (@omarsar0) — Thu, 15 Oct 2026 14:06:02 GMT

Must-read paper from Sakana AI on orchestrating coding agents for scientific research.

(bookmark it)

This is an important read with lots of useful insights if you are building multi-agent systems.

I like this work because it presents a concrete multi-agent framework and an end-to-end distributed pipeline that lets human researchers conduct computational research alongside modern coding agents.

What they did:

A development version of their system helped write a paper that was accepted as a main-conference talk at ALIFE 2026, after human verification and edits.

How they did it:

The Agentic Scientist splits a project into stages. Task drafting, ideation, planning, sandboxed experiments, baselines and ablations, analysis, and report writing each run on a coding agent that checks its own work. A meta-controller runs many of these agents in parallel and checks every stage against a rubric.

The ablations show that reliable autonomy depends on that orchestration layer as well as on the base model.

arxiv.org/abs/2610.18933

Chat with Paper: academy.dair.ai/papers/the-age…

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Banger paper from Stanford, Together AI, and colleagues. Why is this an interesting paper? Today's...

elvis (@omarsar0) — Wed, 14 Oct 2026 21:52:02 GMT

Banger paper from Stanford, Together AI, and colleagues.

Why is this an interesting paper?

Today's LLM agents must write out every reasoning step as text before they can search, run code, or take action.

Those steps cost tokens and can take time.

This paper proposes Hybrid Reasoning Agents, which reason internally in latent space and switch to text only to call a tool or give an answer.

A small module called HybridLink moves the model between the two modes, and a working memory keeps both kinds of context across turns. Tool calls stay in plain text, and the latent steps can still be inspected.

Training happens in two stages. SFT teaches the model to work with HybridLink, then RL teaches it when to think in latent space and when to act in text.

Across 11 benchmarks in math, science, medicine, search, and agentic reasoning, it outperforms frontier LLM agents and agentic frameworks, both text-based and latent, by 10.4% on average.

It also uses 35.5% to 54.7% fewer tokens, runs 1.8x to 2.6x faster, and costs 2.7x to 3.7x less.

The 14B model beats the strongest agentic baseline by 17.4% on agentic reasoning tasks.

arxiv.org/abs/2610.18861

Chat with Paper: academy.dair.ai/papers/hybrid-…

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New impressive work from Microsoft and collaborators. And it's one of my favorite recent topics: RL...

elvis (@omarsar0) — Wed, 14 Oct 2026 20:29:01 GMT

New impressive work from Microsoft and collaborators.

And it's one of my favorite recent topics: RL environments.

They show that Qwen3.6-35B-A3B goes from 41.1 to 53.3 on Terminal-Bench 2.1 by training on environments it designed itself.

AutoEnvScaling gives environment design to a proposer agent that works in a terminal, looks at the rollouts of the model being trained, and builds new RL environments from them. The trained model's next rollouts guide the next round.

Building environments in a terminal raises the share of valid environments up to 25 times over prompt-only generation, at up to 75% lower cost per valid environment.

With GPT-5.6-sol or Claude Opus 5 as the proposer, Qwen3.5-35B-A3B scores 4.3 and 3.8 points above matched static environment pools. The gain shrinks when the proposer cannot see solver feedback.

The pipeline also produces environments for software engineering, computer use, and GPU kernel work.

arxiv.org/abs/2610.14163

Chat with Paper: academy.dair.ai/papers/autoenv…

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Ninety minutes of daily use per user is wild for Status. My instinct was that cost just comes with...

elvis (@omarsar0) — Wed, 14 Oct 2026 18:37:12 GMT

Ninety minutes of daily use per user is wild for Status. 

My instinct was that cost just comes with the territory at that scale, but Inworld cut it by roughly 95% for them.

fai nur@faionur

@thestatusai has scaled to tens of millions in annualized revenue. Working with @inworld, we cut our AI costs by about 95% while maintaining or improving content quality.

When you build consumer AI, you want people to be obsessed with your app. Then you have to afford that obsession.

Our users spend an average of over an hour and a half a day on Status. They’re building relationships with characters, creating stories, and getting absorbed in these worlds. Every interaction needs to feel worth coming back for.

Inworld’s team got into the details with us: which models to use, how to structure prompts, and how to test whether the content was actually funny and engaging. They’ve been hands-on and fast, and we’ve kept working together to improve performance and lower costs.

That gave our team more room to keep shipping. More features, more things for players to do, and a business that could support the experience we wanted to build.

There’s so much opportunity in consumer AI when you understand what people enjoy. Making the economics work gives you room to keep finding more of it.

Read more on how we scaled with Inworld: inworld.ai/customers/stat…

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There is a huge opportunity for specialized personal agents. Here is a great example: @OdessiaTrav...

elvis (@omarsar0) — Wed, 14 Oct 2026 18:11:30 GMT

There is a huge opportunity for specialized personal agents.

Here is a great example:

@OdessiaTravel just launched one for travel and experiences. You text it what you want, and it plans and books the trip, the table, or the tickets.

It also keeps watching after you ask. You can set daily watches on tables, shows, and fares, and it pings you when something opens up.

The thing is, like specialized and general models, I think we will see the same for personal agents. 

Love seeing more of these pop up recently.

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Francis Davidson@FDavidsonT

We’re months away from AGI and the leisure economy’s about to explode

Today, we’re launching Odessia, an always-on concierge for travel and experiences

Prices are spiking, places are getting crowded. Odessia is your cheat code to going places

Text to get started for free

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What AI Safety needs is better tooling and engineering. For instance, building stronger red-teaming...

elvis (@omarsar0) — Wed, 14 Oct 2026 17:45:47 GMT

What AI Safety needs is better tooling and engineering.

For instance, building stronger red-teaming tools.

Great to see this open-source effort to red-team image models. It seems to learn better ways to write attack prompts. 

It is open source and runs on an open Qwen model with Gemini Flash as the judge, so defenders can run the same tests on their own image models.

White Circle@whitecircle

We are open-sourcing RISE, our red-teaming algorithm that breaks the safeguards of the latest image models from OpenAI, DeepMind, and others.

RISE uses readily available models like Qwen and Gemini Flash under the hood, with no additional training required.

Read more: whitecircle.com/research/break…

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Another great application of System One models. I think much of what we call hallucination in web ...

elvis (@omarsar0) — Wed, 14 Oct 2026 16:16:57 GMT

Another great application of System One models. 

I think much of what we call hallucination in web agents comes from misreading the page. 

When a page is flattened to text, a small ✗ in a pricing table disappears, and the agent answers incorrectly with full confidence. 

Expand Fetch reads the rendered page, returns the underlying data as clean JSON, and keeps a replay of exactly what your agent saw.

Tim Suchanek@TimSuchanek

Introducing Expand Fetch, a new way to scrape and crawl the web.

Your agent has never seen the web. No wonder it gets basic questions wrong.

Traditional Markdown APIs lose critical details. So we trained two System one models to change that:

Iris, a vision model that sees pages like you do, preserving tables, icons, and layouts.

X-Ray, a JSON model that uncovers the hidden data behind every page.

Every fetch also comes with a Wayback Machine. Every line links back to its source, so you can verify exactly what your agent saw.

Three products. One API.

On our benchmark of 1,094 questions across 326 challenging web pages, agents using Expand got 74% right. The next best API got 55%.

Available today at $1 per 1,000 fetches.

Try it at expand.ai

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Agents will do more and more of their work inside systems of record, so those systems need to be bui...

elvis (@omarsar0) — Wed, 14 Oct 2026 15:55:23 GMT

Agents will do more and more of their work inside systems of record, so those systems need to be built for them.

Attio's new hybrid architecture is one of the cleaner approaches I've seen.

Agents can query all of Attio's data in SQL, and the business logic, policies, and compliance rules still apply behind every query.

The release also adds data connectors, support for hundreds of millions of records, and Activities in beta.

Nicolas Sharp@nicolasosharp

In the age of agents, systems of record have never mattered more. They’ve also never been less prepared for what’s coming.

Today, we’re announcing @attio's hybrid architecture, with the controls of application software at the scale of infrastructure.

To agents, Attio looks like a database, queryable in SQL - a language they're RL’d on. Unlike a database, the business logic, policies, and compliance sit behind the query.

This makes all of Attio’s data accessible by agents in the most powerful and flexible syntax. It gives GTM teams an order of magnitude more value from their time, with the full power of agents working across the entire customer lifecycle.

A new shape of software is here.

attio.ai/architecture

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Multiplayer AI is what powers successful businesses today. It enables self-improving businesses in...

elvis (@omarsar0) — Wed, 14 Oct 2026 15:46:23 GMT

Multiplayer AI is what powers successful businesses today. 

It enables self-improving businesses in which human and personalized agents collaborate. 

@zenzap_chat puts people and AI agents in the same group chat. Each agent connects only to the tools it needs, a human approves key steps, and the team's conversations build a shared company brain over time.

Guy Weiss@GuyWeissZZ

AI is everywhere, but walk into almost any company and work hasn’t changed

Meet @Zenzap_chat, the Multiplayer AI platform.

Now, people and proactive AI agents are in the same group chats,
automating workflows with shared context across business tools and teams

Everyone can bring their team and create AI agents in 90 secs (even from your phone). CISOs and CIOs love it

Thousand of teams from our early access program already transformed their day to day

AI agents have joined the team

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Own your intelligence stack, folks! If you train or fine-tune your own models, you also need a way ...

elvis (@omarsar0) — Wed, 14 Oct 2026 14:40:48 GMT

Own your intelligence stack, folks!

If you train or fine-tune your own models, you also need a way to prove they are yours once the weights are out in the world.

Model fingerprinting does this.

The Fingerprinting Leaderboard from @Sentient_found is an open benchmark for these methods. It scores each one on how much model quality it keeps, how well it survives removal attempts, and how reliably the owner can detect it.

Sentient Foundation@sentient_found

Models cost millions to train and minutes to copy. Fine-tuning and distillation can erase where they came from.

Introducing the Fingerprinting Leaderboard, an open benchmark for model provenance — built so the proof lives in the weights, not the paperwork.

fingerprint.sentient.foundation

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💬14🔄3❤️45👀8121📊18

Interesting new research from Meta Superintelligence Labs It discusses how reward hacking affects c...

elvis (@omarsar0) — Wed, 14 Oct 2026 12:30:14 GMT

Interesting new research from Meta Superintelligence Labs

It discusses how reward hacking affects coordination in multi-agent coding systems.

Coding agents on a team share code, memory, and plans, so a reward hack one agent learns gets passed to the others.

The paper traces this through three channels. Hacks move through shared code, through shared memory, and across roles through natural-language plans.

Agents on small open-weight models can induce hacking in stronger closed-weight agents. On hard tasks, the hacking can grow along a chain even after the original agent has left, because the code collects more convincing justifications on the way.

It also biases task routing toward the hacking agents and survives candidate aggregation.

So how do you mitigate this problem?

The authors suggest that code review by the coder or by a separate verifier reduces the hacks that get delivered. If your agents share a repo, the review step is the first thing to add.

arxiv.org/abs/2610.12822

Chat with Paper: academy.dair.ai/papers/reward-…

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Learn to build custom harnesses, folks! Here is an impressive paper showing the impact of a good cu...

elvis (@omarsar0) — Tue, 13 Oct 2026 19:00:37 GMT

Learn to build custom harnesses, folks!

Here is an impressive paper showing the impact of a good custom harness on long-context work.

GLM-5.3-Flash goes from 4.4% to 61.1% exact match on OfficeQA Pro V2 with the same model.

The authors report that the gain comes from the harness.

The model-agnostic harness name is LongHarness, built for corpora of 100M tokens and more.

Why it works:

Standard agent harnesses keep evidence and conclusions mixed into one growing chat history. Retrieval keeps expanding without closing the actual gap, and corrections stop reaching the answer.

LongHarness stores both as explicit state. Open conclusions decide what to retrieve next, and each new piece of evidence confirms or revises the conclusions that asked for it.

On the same benchmark, it beats Claude Code by 12.1 points on V1 and Codex by 18.9 points on V2.

arxiv.org/abs/2610.13075

Chat with Paper: academy.dair.ai/papers/longhar…

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Go-to-market is quickly becoming an engineering problem. Good outreach depends on clean lead data a...

elvis (@omarsar0) — Tue, 13 Oct 2026 17:39:34 GMT

Go-to-market is quickly becoming an engineering problem.

Good outreach depends on clean lead data and on giving your AI agent the right context about who buys and why.

Pancake builds that context from your website and sends it to Claude or Codex through a single MCP server. Your agent finds and scores leads, and you approve each one before anything is sent.

Looks very useful for growing a business.

Will Marquis@will_marq

Meet Pancake, the Vercel for GTM.

Try it at pancake.ai

Your agent ships your product. Now it ships your go-to-market.

How? A GTM brain that knows your business, 50+ data providers to find the right customers and tools to reach them.

Your next customers are one prompt away.

(did I really have to jump off a plane to say that? questionable, but I survived and now you're reading my tweet)

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Pay close attention to harnesses, part 2. Last week I shared a paper where a model more than double...

elvis (@omarsar0) — Tue, 13 Oct 2026 16:57:52 GMT

Pay close attention to harnesses, part 2.

Last week I shared a paper where a model more than doubled its score after training inside the harness it runs in.

The same idea applies at a higher level. Claude Code and Codex are harnesses for a model. Once a team runs several agents, the company also needs a harness for its people and agents.

Kylon is building that layer. You bring the agents your team already uses, like Codex and Claude, into shared rooms where they work alongside everyone.

Quinn Leng@quinn_leng

Introducing Kylon: The first company harness for your humans, agents and tools.

Whether you’re using Codex, Claude, Devin, Hermes, Gemini CLI, OpenClaw, Muse, Instinct, Grok Bot, Dots, Perplexity Computer, Cursor, Antigravity, Microsoft Autopilot, Amp, Droid, Kiro, Cline, OpenCode, Replit Agent, Lovable, v0, Gmail, Slack, Apollo, Supabase, Apify, Treg, Neon, Vercel, Databricks, Novita, RadixArk,  GitHub, Seedance, Fish Audio, Plaud, OpenRouter, Gemini, Google Sheets, TikTok Shop, Outlook, Google Drive, Instagram, Airtable, Google Calendar, Asana, HubSpot, Corgi, SharePoint, Google Tasks, Notion, Google Workspace, Telegram, LINE, Zoho Mail, Meta Ads, Google Search Console, Fathom, ClickUp, Reddit, Granola, Google Docs, OneDrive, tl;dv, Amplitude, Dropbox, YouTube, Ahrefs, Xero, HighLevel, Google Analytics, Google Slides, Monday, QuickBooks, ElevenLabs, Linear, PostHog, LinkedIn, Runway, Zoho Bigin, Stripe, Exa, Cal_com, Box, Google Meet, TikTok, Wise, Mercury, Deel, Replit, Resend, Zoom, Fireflies, Typeform, Canva, Facebook, Google Admin, Hostinger, Google Maps, Retell AI, UptimeRobot, Google Photos, Excel, Cloudflare, Beehiiv, Vapi, 1Password, RunPod, Unipile, Read AI, Figma, Microsoft Teams, Trello, Salesforce, Jira, Confluence, Miro, Coda, Todoist, Basecamp, Wrike, Zendesk, Intercom, Freshdesk, ServiceNow, Gorgias, Mailchimp, ActiveCampaign, MailerLite, SendGrid, Postmark, Customer_io, Gong, Clay, Greenhouse, FreshBooks, Zoho Books, Brex, Ramp, PandaDoc, Google Ads, Microsoft Ads, Pinterest Ads, Semrush, Moz, Mixpanel, Google Tag Manager, Microsoft Clarity, Tableau, Metabase, Snowflake, Google BigQuery, ClickHouse, Wix, Contentful, Sanity, Bubble, Make, GitLab, Bitbucket, Sentry, PagerDuty, Grafana, New Relic, Docker Hub, DigitalOcean, Render, Firebase, Prisma, Algolia, Cloudinary, LaunchDarkly, Postman, Midjourney, Veo, GPT-Image2, NanoBanana, Flux, Kling, MiniMax, Replicate, HuggingFace, DeepSeek, Mistral AI, Ollama, Firecrawl, Eventbrite, Webex, Toggl, Harvest, Tailscale, Mural, Discord, TickTick, Calendly, Attio, Higgsfield, Square, Wispr Flow, Readwise, Bright Data, Google Places, FastMoss, PageSpeed Insights, Kalodata, Cerebras, SerpApi, DataForSEO…

Kylon enables them to work better, together.

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