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The Computomatix Times

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Edition of Thursday, July 23, 2026

3 stories

Andrew Ng Announces OpenWorker, an Open-Source Work Agent

Andrew Ng Announces OpenWorker, an Open-Source Work Agent▶

Andrew Ng announces OpenWorker, an open-source agent for Mac that produces finished deliverables such as documents, Slack messages and calendar updates. It is model-agnostic, supports local models via Ollama, and is available on GitHub with Windows support planned.

Original post · 1 min read
Announcing OpenWorker! An open-source agent that doesn't just chat with you, but delivers finished work -- like hand you a polished document, send a slack message, or update a calendar entry.

Ask it to prepare a customer brief, untangle your calendar, draft a report, or triage a Slack alert. It works across your files and everyday tools, produces the deliverable, and checks in before doing anything consequential.

OpenWorker runs on your Mac, with Windows support coming soon. It does not lock you into any one model. Bring your own API key and run it with GPT 5.6 Sol, Claude Fable, Gemini 3.6, an open weight model (like Kimi, GLM, DeepSeek, Inkling), or Ollama to keep your data local. Your data does not leave your machine except through an LLM provider and integrations that you choose.

@rohitcprasad and I are building OpenWorker because AI coworkers are an important way to get work done, and we want there to be an open, privacy-preserving, model-independent option. Check it out and let us know what you think!

Try it out: openworker.com (requires your own API key)
Source code: github.com/andrewyng/openworker
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AI8/10

Satya Nadella Outlines Microsoft's MAI Models and Cost-Efficient Routing

Satya Nadella's article argues that optimizing cost-to-outcome matters as software gains marginal cost, describing Microsoft's MAI model family. He says MAI models now outperform some frontier models on product tasks with fewer tokens and are being routed across GitHub Copilot, Excel and Outlook.

Original post · 3 min read
X ArticleFrontier Diffusion & Control
In a world where software has real marginal cost for the first time, how do we ensure frontier benefits are diffused across the entire ecosystem?
The key is to optimize the cost-to-outcome frontier in real world context. In practical terms, that means using the right model for each task, and optimizing the context, skills, tools, and agent harness around it.
This is the motivation behind our MAI model family. These models have been built ground up with clean data lineage and optimized for learning transfer from generalist to specialized skills in enterprise RLEs. We continue to make rapid progress in this pursuit.
We can now take saturated frontier capabilities and deliver them at scale and at lower cost through models optimized for high-usage products, while continuing to use frontier models for frontier needs. We are proving this out across our first party products, and thereby creating a template for every other AI native, SaaS, or Enterprise company out there.
In our products, frontier models from OpenAI and Anthropic are part of the orchestration system alongside MAI. But the model is only one part of the hill-climbing system. Harness, memory, context, tools, skills, user interactions, etc. all shape the evals and performance of these agentic systems.
The other key criteria to ensure that you are in control, is your evals should continue to hill climb even when any given model has been removed. Therefore we build RLEs where models learn inside the product system and are rewarded for completing the tasks customers actually care about. We train models against the actual product harness, interactions, and outcomes they will encounter. And strategically ensure that the harness, memory, context, skills are externalized outside of the model.
Product-specific evals and model independence give us the control and a direct hill to climb, and to keep refining until we reach the right quality-cost target. We are now seeing MAI models outperform general-purpose frontier models in many use cases while using a fraction of the tokens.
We believe the biggest opportunity is to optimize all of these layers together in the products where the world works every day. And we are beginning to route traffic across our first-party surfaces to MAI whenever our models match or outperform frontier alternatives.
We are seeing promising early results across GitHub Copilot, Excel, and Outlook and are beginning to take the same approach across Copilot Chat, PowerPoint, and more. And all these results will only get better as the entire system keeps hill-climbing!
What we are doing across our first party products is also what every enterprise customer can be doing in their real world agentic systems with their proprietary evals, their proprietary RLEs, workflows, and context. We are making all this available as part of Foundry and our toolchain.
Read more here: microsoft.ai/news/hill-climbing-mai-models-for…
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Max Anderson Criticizes Google's Search Pricing and Keyword Changes

Max Anderson argues that Google's search revenue growth is artificial, citing the silent end of second-price auctions so advertisers pay their full bid and reduced keyword targeting precision. He calls these tactics extractive as LLM queries cannibalize legacy search volume, responding to Alphabet's Q2 results.

Original post · 4 min read
As someone who has personally spent $500k / mo+ on Google Ads for years, I can tell you with certainty:

This revenue growth in Search is artificial & extremely unhealthy for Google’s business long term

Search volumes are declining as legacy search is being increasingly cannibalized by non-monetized LLM queries

Google’s response?

Manufacture revenue growth via short-sighted, highly extractive, customer-hostile tactics. I.e. charge advertisers more for lower quality clicks, including clicks they do not want and explicitly did not approve Google to charge them for

A few examples to illustrate:

For all of its history until recently, Google operated on a 2nd price auction model

I.e. if you bid $5 CPC and the next highest bidder bids $1 CPC, Google charged you $1.01 for the click (one penny more than the 2nd highest bidder) rather than the $5 you bid

This was a genius move by Google early on as it incentivizes advertisers to input their true maximum willingness to pay rather than trying to play the game of bidding low and constantly adjusting to try to stay just ahead of the next highest bidder while still not paying too much

However recently, Google silently deprecated the 2nd price auction and began charging advertisers as much as their bid and budget caps allow, regardless of what anyone else is bidding

It’s a short-sighted cash grab at the expense of the long term health of the advertiser ecosystem

Making thing worse, Google also recently nerfed keyword targeting precision

Google previously had precise keyword targeting settings that allowed advertisers pick individual search phrases to bid on, defined down to the character w/ exact match or phrase match targeting

This was one of the core features that made search advertising magic, enabling advertisers to run extremely precise campaigns based on exactly what their target customer typed

But now, even if you bid on a specific term or phrase using the strictest exact
-match targeting settings, Google will show your ad across 1000’s of unrelated keywords, labeling them as as “exact match (close variant)”

The definition of “close variant” means whatever they want it to and changes constantly. The result is advertisers get billed for clicks that are totally irrelevant to their business and that their targeting settings explicitly forbid Google from targeting. Google does it anyway and there’s no ability to turn this off

So now exact match is broad match, and broad match is just meaningless spam

This is all very bad for advertisers, but for Google, it allows them to show your ad and bill you for clicks across 1000x more searches that were previously going unmonetized (mainly because they’re garbage queries no one wants)

This is how you grow revenue atop declining search volumes

Lastly, and perhaps most egregiously, Google quietly stopped respecting budget caps by a factor of 2x. For example campaigns we’ve been running for years with $1000 daily budget caps suddenly began spending $2000+ per day

And the extra spend is entirely on the garbage keywords Google arbitrarily throws in as “exact match (close variants)” which have no value to our business, but can’t be turned off

Google offers no refunds nor any recourse for overspend or spend on keywords you explicitly did not target

These are not the actions of a healthy business. These are the actions of company whose core business is in decline but desperately needs to pump quarterly earnings so Wall Street will continue to fund insane capex while hopefully looking through their rapidly deteriorating negative free cash flow

Google operated a benevolent monopoly for the better part of 25 yrs

Meaning the value Google captured from Search was but a small fraction of the value it created, and that spread produced a potential energy that justified expectations of high earnings growth far, far into the future

This is now no longer the case

At the alter of AI capex, Google is sacrificing the golden goose
Sundar Pichai @sundarpichai
Q2 was an amazing quarter, with our AI investments redefining what’s possible across every part of our business.

Alphabet revenue grew 24% YoY and Google Cloud accelerated to 82% growth. We saw exciting momentum across the board from Search to YouTube to the Gemini app (which reached 950M monthly active users). Our model APIs are processing 22B tokens/min (up from 16B+ last quarter) driven by our workhorse Flash models. We’re also seeing great adoption of Gemini Enterprise, used by 90% of the Fortune 100, as well as strong demand for our security solutions.

Outstanding results and momentum, …
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