Watch this space.
Announcing Today - a team knowledge base, curated by Copilot and agents - powered by SharePoint!
Finally, the tech is here to build a feature I've dreamed of for a long time! Excited to see early adopter feedback.
techcommunity.microsoft.com/…
A modern retrieval stack is increasingly
Itself an agent
Let the query decide: Introducing "auto", a new retrieval reasoning effort mode in Foundry IQ that recovers 97.6% of the evidence recall of the highest effort level while using 46.9% fewer query planning tokens.
techcommunity.microsoft.com/…
On land & sea, "globalized markets" require a ton of specialized transport, legal structures, currency management & more.
In space, it's the default.
Not sure if people realise Starlink has an incentive to sell any unused satellite capacity at anything above $0 in areas where constellation is not fully utilised... otherwise the satellites would generate no revenue when passing over those areas. Prices (adjusted for service quality) will undercut incumbents in countries with cheap but poor coverage (Africa, Asia..... India...)
This is a massive contrast to the unit economics of terrestrial fibre/cable...
Great summary of 10 years of progress in specialized models for Doc Processing - commandline.microsoft.com/az…
The bitter lesson is coming for harnesses and middleware....
New Apple paper basically says progress in automated ML engineering has come from models and runtimes rather than the scaffolding built around them.
It finds one well-prompted coding agent with shell and file access matched or beat 4 multi-agent ML systems, so skip the orchestration and start with a single session.
Those systems add search trees, memory layers, and specialist agent teams.
They were designed when a model could only write code, not run it.
This paper re-ran every wrapper on 1 codebase with the same model, hardware, and 24-hour budget. Giving the model a shell instead of a chat box was the only change that clearly mattered.
With GLM 5.2, the minimal agent medaled on 62.5% of Kaggle tasks against 47.1% for the best published harness. Adding parallel agents and a message channel actually dropped its medal rate from 55.7% to 33.3%.
– arxiv. org/abs/2609.40303
Title: "How Much of a Harness Does a Strong Agent Need for Autonomous ML Engineering?"
Graphlit had a great run
Upside - we are very excited to have kirk on board at msft working on context graphs. :-)
Graphlit went offline yesterday.
Five years. 50+ integrations, a temporal knowledge graph, durable agents, paying customers in production since 2023.
The product worked. The business didn't. Both are true.
Agents are still only as good as their context. That problem isn't going anywhere.
The tech and IP are still available as an asset purchase, for now.
Thank you to everyone who built on it.
Vinod Valloppillil retweeted
Graphlit went offline yesterday.
Five years. 50+ integrations, a temporal knowledge graph, durable agents, paying customers in production since 2023.
The product worked. The business didn't. Both are true.
Agents are still only as good as their context. That problem isn't going anywhere.
The tech and IP are still available as an asset purchase, for now.
Thank you to everyone who built on it.
Congrats to the team. One of the more interesting counter-trends has been the new appreciation & energy in specialist, task models in the face of the large, generalist model onslaught.
Translation, STT/TTS, Docs/OCR, Classifiers & others (Jev!) have been steadily growing businesses for years and a few have even accelerated (11labs speech revenue perhaps being the most publicly referenceable #'s).
We're launching the most accurate real time transcription model in the world... #1 !!!
55% faster and 60% cheaper than ElevenLabs.
Come build agents on our platform!
With richer models, more iterative search, and bigger context, RAG increasingly boils down to large, coarse, overlapping chunks
Perhaps the single biggest difference between Consumer and Enterprise AI is the need for inspectability of the beginning, intermediate and final work products
i dont think post-training will make md files redundant
i want to be able to read what my agent is working with and check if it makes sense (legibility)
model weights are a black box. you cant read through them and check what the model has learned
think about all the decisions in a company and how they depend on each other
when you work with other people (or agents), you want those decisions and relationships written down somewhere so you can check them and agree on what they mean
of course some procedural skills may become less useful. but you still need a surface where you map out what you know in a way thats verifiable for you
pretty sure markdown will stay because its a format that both humans and agents can read, inspect and edit
we just need to stop writing slop into md files
context needs to be highly curated and checked against your actual expectations and understanding
linked and TYPED markdown files let you model your knowledge / understanding in a graph
damn it, just stop writing slop and start modeling your knowledge
For the 2 mega, umbrella tech markets -- Consumer + Enterprise -- AI is rapidly transitioning from disruptive to sustaining innovation.
There was a brief, glorious, ~3 yr window where AI innovation alone was enough to catapult you to the upper echelons of these segments.
OpenAI has a genuine shot at being the next great, long term durable consumer brand alongside Google, Facebook, and Apple. Anthropic similarly might find itself the next major enterprise vendor alongside Microsoft, SFDC, AWS & others.
There are, of course, still plenty of smaller, niche markets where AI disruption is still in full force (legal, creative, customer support, etc.). But the winner's circle for mega-consumer and mega-enterprise is quickly getting smaller.
"Network Computers" have been pushed on consumers for ~30 yrs. They've been built on various substrates -- Java, HTML5, Netbooks, Handheld-PCs, Virtual Desktops, Tablets & more.
An AI chat bot talking to a full, virtual machine in the cloud is the latest form factor
I’ve seen a couple of posts about this so wanted to demystify. Today, every Muse user gets a free computer in the cloud. It's a real computer, and we’ve designed the security architecture of the Muse Secure VM carefully so you and your Muse can do almost anything you could with a computer sitting under your desk while keeping you and the system safe from threats like prompt injection. We wrote about this at length in our security blog post – security.muse.ai. Activity in the “runtime cell”, which you share with your Muse is unfettered, but sensitive actions are all overseen by the Sentinel, which runs outside of that cell. Similarly, all sensitive secrets - like the passwords you enter into Muse’s secure credential storage - are also stored outside the runtime cell.
The runtime cell gets its own root filesystem (including a full Ubuntu linux image) separate from the host filesystem where your other more sensitive data lives. Because it is isolated from the sensitive stuff that runs on the same box, this means that we can, and do, offer users full visibility and control over the files in the runtime cell. Just as you can when you install Linux on your home computer, you can poke around and see all the files that make the system work - both debian system files and the binaries and data files that implement the parts of Muse which run in the runtime cell.
This was a very deliberate choice - your Muse Secure VM truly is your own computer in the cloud. You can install software in it, write and compile code, use the browser to surf the web: it is your own Linux box that you can operate as you choose with your Muse. Poking around in this computer doesn't give you any privileged access to Meta infrastructure, or to other people's data
If I may geek out a little here for a second… As a kid I loved to take things apart to see how they worked. As a teenager I got into computers and soon found myself drawn to C:\WINDOWS\SYSTEM and the system registry, later Slackware’s /dev/, /proc/ etc – I could see how the system was laid out and as I explored what DLL files and .so files actually did, I gradually became able to meld the computer to my own will.
We’re really proud to be able to put a real computer in millions of people’s hands with a similar level of transparency. We built a file explorer right into the Library tab of the UI. We want you to be able to see the markdown files Muse writes while it thinks about how to serve you better, and explore the internals of the system if you’d like to.
So, when you ask your Muse to show you its entire filesystem, and receive gigabytes of files you’re seeing the full contents of the runtime cell. It’s yours to explore and enjoy!
If you’re not a geek like me, or simply want to download the data that you personally have created directly with your Muse, we added a feature for that too in Settings > Data controls > Download your agent data.
Meta Muse is one of several indications that AI has entered a new chapter. Frontier Math capabilities (for ex) deliver incredible demos and will have a huge Halo. However, tightly integrated agent experiences built around Workhorse AI will make the real $$$.
Ben Thompson calls Meta's Muse a bearish signal for the frontier labs, because a not-state-of-the-art model now makes a product stickier than any chatbot
"The key for the frontier labs, then, is to build those user touchpoints while they have superior capabilities."
"However, this is where Meta's recent launch of Muse is a bearish signal. Muse is, by a significant margin, the best and most approachable personal agent product I have tried."
"Meta deserves a tremendous amount of credit for the product work they have put in, as well as the massive infrastructure commitment entailed in providing users with a very capable virtual machine for free."
"Oh, and of course they deserve credit for the Muse Spark model undergirding Muse."
"That noted, Muse Spark 1.3, the most advanced Meta model, is still not state-of-the-art, and that is the bearish signal: it is good enough for a very good personal agent product, and critically, a personal agent is much stickier than a chatbot."
"Once you have put all of your information into a personal agent and actually incorporated it into your day-to-day life, it is much more of a challenge to change to something else."
While many Pre-LLM specialist models got Bitter-Lessoned into giant generalist LLMs, an interesting handful (speech, translation, and document AI...) have become real businesses with increasing traction
Cost, speed, DX, and reliability still matter (for now).
Jev is a textbook example of Clayton Christensen’s Innovator’s Dilemma playing out in AI.
It is commoditizing the bottom of the ML market: classifiers, routers, scoring and context triage. For frontier LLM labs, these workloads are not even worth pursuing. Revenue per decision is tiny, margins are razor-thin, and their entire infrastructure is optimized around selling expensive reasoning and token generation.
They cannot simply put a smaller LLM on fast inference chips like Cerebras and compete. Jev uses fundamentally different infrastructure, built to return typed probabilistic decisions so it’s a pure disruption
Speaking of / on specialist models -- I'll be talking about Translation AI at #SFTechWeek --
Details - partiful.com/e/nalVTU28EjvOX…
This is a spectacular case of picking the single highest value niche market for a focused v1.0 (robots for data center ops)
We’ve raised $80 million in Series A funding, co-led by the Valor Atreides AI Fund and Hummingbird Ventures, with continued participation from Conviction, Abstract, A*, and Grant Gordon. This brings our total raised to over $100 million.
We started Watney by asking a simple question -- what can a robot enable in industries where the work remains valuable on an infinite horizon? We found an answer in the fundamental inputs to civilization: energy, matter, and intelligence.
A robot will never be as charming as a barista or as personable as a housekeeper, but it can be more exacting than a surgeon. And, a breakthrough in one quickly becomes the capability of millions. We don’t imitate human motions or processes. We care about the new, differentiated capabilities robots can unlock. We work on problems where our choice of embodiment gives us an order of magnitude advantage, whether through precision, reliability, or scale.
Compute is just beginning to transform society, but it’s constrained by execution. Since 2025, Watney has been serving the largest hyperscalers in the world, helping accelerate their compute rollout through an end-to-end deployment model.
Across hundreds of thousands of hours in customer facilities, our systems have achieved more than four nines of reliability. They’ve enabled our customers to build data centers faster, at larger scales, and with more ambitious architectures. Today, we operate the largest fleet of dexterous robots running 24/7/365 across the United States.
This is all to say, it's an exciting time at Watney. We’re all here because we want to see more ambition in the physical world. We are hiring across all domains. Join us.
Vinod Valloppillil retweeted
The vibes in SF feel pretty frenetic right now. The divide in outcomes is the worst I've ever seen.
Over the last 5yrs, a group of ~10k people - employees at Anthropic, OpenAI, xAI, Nvidia, Meta TBD, founders - have hit retirement wealth of well above $20M (back of the envelope AI estimation).
Everyone outside that group feels like they can work their well-paying (but <$500k) job for their whole life and never get there.
Worse yet, layoffs are in full swing. Many software engineers feel like their life's skill is no longer useful. The day to day role of most jobs has changed overnight with AI.
As a result,
1. The corporate ladder looks like the wrong building to climb.
Everyone's trying to align with a new set of career "paths": should I be a founder? Is it too late to join Anthropic / OpenAI? should I get into AI? what company stock will 10x next? People are demanding higher salaries and switching jobs more and more.
2. There’s a deep malaise about work (and its future).
Why even work at all for “peanuts”? Will my job even exist in a few years? Many feel helpless. You hear the “permanent underclass” conversation a lot, esp from young people. It's hard to focus on doing good work when you think "man, if I joined Anthropic 2yrs ago, I could retire"
3. The mid to late middle managers feel paralyzed.
Many have families and don't feel like they have the energy or network to just "start a company". They don't particularly have any AI skills. They see the writing on the wall: middle management is being hollowed out in many companies.
4. The rich aren’t particularly happy either.
No one is shedding tears for them (and rightfully so). But those who have "made it" experience a profound lack of purpose too. Some have gone from <$150k to >$50M in a few years with no ramp. It flips your life plans upside down. For some, comparison is the thief of joy. For some, they escape to NYC to "live life". For others still, they start companies "just cuz", often to win status points. They never imagined that by age 30, they'd be set. I once asked a post-economic founder friend why they didn't just sell the co and they said "and do what? right now, everyone wants to talk to me. if i sell, I will only have money."
I understand that many reading this scoff at the champagne problems of the valley. Society is warped in this tech bubble. What is often well-off anywhere else in the world is bang average here.
Unlike many other places, tenure, intelligence and hard work can be loosely correlated with outcomes in the Bay. Living through a societally transformative gold rush in that environment can be paralyzing. "Am I in the right place? Should I move? Is there time still left? Am I gonna make it?" It psychologically torments many who have moved here in search of "success".
Ironically, a frequent side effect of this torment is to spin up the very products making everyone rich in hopes that you too can vibecode your path to economic enlightenment.