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My wife and I are so very proud of our daughter Dr. Gillian Choquette for obtaining her PhD.ππ»πππ»π
So much has changed In FellsPoint and the Baltimore inner harbor.
Carl Dea β»βπ retweeted
Every Mac on macOS 27 now has a free LLM built into the Terminal.
No account. No API key. No internet. No token bill.
Setup (1 minute):
1. sudo fm license (enter your Mac password, one time)
2. fm available (checks the model is downloaded)
3. fm respond βyour questionβ
Works on any Apple Silicon Mac, M1 included.
- Pipe files: cat notes.txt | fm respond -i βsummarize thisβ
- Get JSON back: build a schema with fm schema object, pass it with --schema
- Local API: fm serve gives you an OpenAI-compatible endpoint on localhost
- Resume chats: sessions save to ~/.fm/sessions/
The limit is 4,096 tokens per session, and your prompt, instructions, and the answer all share it.
So skip code and math. Use it for summarizing, extracting, classifying, and rewriting.
Free, private, offline, and already on your Mac.
Carl Dea β»βπ retweeted
Replying to @OpenJDK
@OpenJDK The list of JDK 28 looks very intresting JEP 543 Structured Concurrency is now finalized (Status: Integrated)
openjdk.org/projects/jdk/28/
Carl Dea β»βπ retweeted
Fetching JSON from an API and mapping it to records in plain Java.
No Jackson. No Gson. No dependencies.
Just HttpClient and the new JSON API coming in JDK 28 (JEP 540). π
github.com/danvega/native-jsβ¦
Introducing @Pyronautfw β a high-performance framework for Python, built with Micronaut, GraalVM, and GraalPy!π
By @graemerocher and @thomaswue at #Devoxx: youtube.com/watch?v=zo6L7bxnβ¦
Carl Dea β»βπ retweeted
Start in 1972. After the 1970 census, Maryland drew congressional maps that respected geographic and political boundaries. The result was a delegation of 4 Republicans and 4 Democrats, a genuinely competitive split reflecting Marylandβs actual political makeup. Fair maps produce fair elections.
Ed Zitron: Microsoft Has $80 Billion Of AI Chips It Can't Plug In youtu.be/es4FfRU8saQ?is=GYW1β¦ via @YouTube
Carl Dea β»βπ retweeted
WebMCP by Microsoft and Google is HERE and I'm surprised more people aren't talking about it.
Why does it matter?
BILLIONS of dollars are about to move through agents, and the internet isn't built for them yet.
1. SEO was about Google understanding your page.
2. AEO was about AI citing you.
3. WebMCP is about the agent actually finishing the job (buying, researching, requesting quote etc).
WebMCP is basically websites with agent buttons.
Instead of an agent scanning a page like a human, screenshotting and guessing where to click, the site just tells it "here's how to search, here's how to book, here's how to buy."
2 cash flowing businesses you could start today using WebMCP:
1. A WebMCP conversion agency
Make boring business websites agent-ready. Law firms, HVAC, med spas, dentists. Build them the first tools (request a quote, book a consult), sell the setup for $2k, then charge a few hundred a month to monitor and improve it.
2. An agent mystery shopper.
Test whether agents can actually complete the important journeys on someone's site, buying the hoodie, booking the consult, filing the claim. Hand them a report on where the agent got stuck, missing tools, bad descriptions, lost conversions. Charge monthly, then turn the repeated fixes into software.
Full episode on @startupideaspod with @hot_town below
youtu.be/EoNH3Tn8wYE?si=vqaNβ¦
You'll learn what WebMCP really is, see examples of it IN ACTION, and hear more about 2 startup ideas using WebMCP you can start today.
WebMCP is pretty cool.
Watch.
Carl Dea β»βπ retweeted
The AI containment debate may already be over.
@friedberg argument is that powerful AI is no longer controlled by a handful of American companies running enormous data centers.
Open weight models are becoming capable and efficient enough to download, modify, and run on consumer hardware, making the technology nearly impossible to contain once released.
The recent launch cycle shows how quickly this is happening.
DeepSeek released a faster and cheaper model, Alibaba published downloadable weights for Qwen Image 2.1, and PrismML compressed a 27 billion parameter Qwen model into just 5.9 gigabytes.
PrismML says its compressed model retained 98.2% of the original modelβs benchmark performance while becoming small enough to run on a laptop or consumer GPU.
At the same time, closed model companies continued releasing more capable and affordable systems.
Anthropicβs Opus 5.5 reportedly matched its more expensive flagship while costing approximately 40% less to run than its predecessor.
Stanford found that the cost of running a model with GPT 3.5 level performance fell more than 280 fold in roughly 18 months.
It also found that the performance gap between leading open-weight and closed models narrowed from 8% to 1.7% on some benchmarks in one year.
That is the real meaning of Friedbergβs argument.
Governments can regulate large data centers, chip exports, and commercial AI providers but they cannot easily remove models that have already spread across the internet and onto personal computers.
The debate must therefore shift from whether powerful AI should exist to how it should be used and who becomes responsible when it causes harm.
Major Flooding Hits Ocean City, MD - Streets Under Water - Ocean's Calli... youtu.be/Zbl62AIqmms
Unlock the 11 Times Table Trick #shorts youtube.com/shorts/Sw-pRuuV1β¦
a Stanford professor used JEV to build a data science system that analyzes 40.000.000.000 data points every 15 min
his system can process massive datasets and automatically decide which results deserve deeper analysis.
the professor spent more than a year developing the system with his research team.
the first version relied heavily on LLMs to interpret every result.
it worked, but analyzing millions of records created unnecessary latency and huge inference costs.
then they redesigned the decision layer around Jev.
instead of generating explanations for every data point, Jev evaluates the results and decides what the system should investigate next.
I found a breakdown of the pipeline they used and the way Jev filters the data is surprisingly clever.
Iβll show you how it works next so stay tuned if you want to see what Jev Engineering looks like inside a real data science system.
would you let an AI decide which patterns in your data are worth investigating?
Quite brilliant. This will be the standard to follow. Once stable I hope there is an open source on prem version.
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI?
Iβve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev
β’ 20-200x faster
β’ 40-400x cheaper (w/ output tokens free)
β’ Frontier composable intelligence optimized for decisions
AFAICT the shortest path to AI-based economic revolution
A great example of what Ai should be doing: understanding biological sciences (and other sciences) and discoveries to help humanity.
What your cells actually look like (it's chaos) - Eric Betzig youtube.com/shorts/MgLQiIIk1β¦
Pardon the cursing.
Not every textbook(LLM) is accurate.
Carl Dea β»βπ retweeted
Caveman Issues Dire Warning That The Wheel Is Going To Eliminate Countless Jobs buff.ly/u9nCQoL
MIT Just Discovered What Jobs AI Actually Killed (by Dylan John) youtube.com/watch?v=UGVz615Aβ¦
Carl Dea β»βπ retweeted
Day 12 building the Figma for hardware until somebody invests in it.
This week Iβm focusing entirely on jewelry.
Technology is getting closer and closer to our bodies, and I think the objects around it will matter just as much as the technology itself. A necklace, ring or charm can carry something digital without asking for your attention all the time.
So I want to explore the space between hardware, jewelry and everyday objects: connectors, chains, rings, charms, carriers and ways of turning technology into something you actually want to wear.
The system Iβm building will eventually go beyond devices. I want it to be a way to design objects in general.




























