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Jonathan Dunlap
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Jonathan Dunlap
@JonathanRoseD
Research & development project manager | Building tools for community | Adobe and Tachyon alum 🦦🇺🇸
Ann Arbor, MI Katılım Nisan 2008
3.9K Takip Edilen1.9K Takipçiler

Moving from substance to invariances was an idea of Deleuze. It seems any object can be defined as a container of Differences (see Deleuze) that remains stable over time. "I am not other people, therefore this Difference-container is what I call me." essentiafoundation.org/how-is-an-elec…
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@thdxr @jlongster Can this be added into OpenCode?
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used a trick @jlongster came up with
agents can control browsers but you can also ask it to record network requests into a HAR file
then it can derive a client for any website which is more efficient than browser controlling it every time
made it build a quick uber eats cli

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Jonathan Dunlap retweetledi

Introducing MorphoHDL, a minimal language prototype for growing boolean circuits! paradigms-of-intelligence.github.io/morpho/
Early this year I wanted to design a size-agnostic graph rewrite rule system that could build functional boolean circuits. First I thought about "chemistry"-like reactive systems, but rules were too complicated with many different node types carrying multiple indices...
Then I realized, that cell division is much more natural way of building complex structures, and once I started to treat graph edges as buses instead of single wires, everything clicked. No new formalism — just taking good old recursion and seeing how far I can push it. Ripple-carry adder, Brent–Kung adder, multipliers ... and some Haeckel-esque creatures along the way! Hope you enjoy the report of my journey and the demos.

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Jonathan Dunlap retweetledi

interesting position paper throwing cold water on autoresearch/ai scientist: LLMs can't jump.
The thought experiment is this: Take an LLM with a 1905 knowledge cutoff. Feed it every paper, every dataset, every equation of that era. Could it invent general relativity?
No.
Discovery isn't one thing. It's three. You can induce — generalize from data, which lands you at Newton plus some epicycles to explain Mercury's weird orbit. You can deduce — derive rigorously from axioms you already have, which never gives you new axioms. Or you can jump — invent the frame itself, decide that spacetime curves. That third move is the one that matters, and it's exactly the one induction and deduction can't reach.
Penrose put it as three worlds: Physical, Mental, Platonic. Data flows from the world into a mind fine. But the new law has to be discovered into the Platonic world first — and that step is the jump. LLMs are induction machines running over what already exists. Structurally, they don't take it.
I think it’s a warning to AI scientists/autoresearch against collapsing two very different things into one word.
Hill-climbing: LLMs are already superhuman here, and autoresearch in this sense is real and moving fast.
Abduction/leap/jump: a new frame that reorganizes the field, that is a different act entirely, and nothing about scaling induction suggests you get there.
Most of what Autoresearch ships today will be spectacular hill-climbing. The jump is still ours for now.

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@wuming_dao33582 @typescript Thanks, just watched an interview. The circular deps seems a key concern to move to Go as the runtime can nicely handle them.
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@JonathanRoseD @typescript anders have more video at YouTube say it choose for why 😂
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📣 The moment is here. 📣
TypeScript 7 is officially released! 7️⃣
devblogs.microsoft.com/typescript/ann…
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@ahejlsberg @typescript Congrats to you and the team Anders! Huge milestone for the future of TS. I can't wait to see what's in store for v7.5 or v8. (pssst Pipeline Operators!)
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Huge milestone for our team today: TypeScript 7 is now generally available--a native port that runs 10x faster. @typescript devblogs.microsoft.com/typescript/ann…
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TypeScript remains as one of the most productive technologies of the web. I've maintained JS projects with 100k LoC, and it was utter nightmare fuel to refactor anything safely.
TypeScript@typescript
📣 The moment is here. 📣 TypeScript 7 is officially released! 7️⃣ devblogs.microsoft.com/typescript/ann…
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Jonathan Dunlap retweetledi
Jonathan Dunlap retweetledi
Jonathan Dunlap retweetledi
Jonathan Dunlap retweetledi
Jonathan Dunlap retweetledi

Continual Harness: An Efficient Self-Improving Agent on ARC-AGI-3 by @sethkarten from @PrimeIntellect
> The heavy test-time learning required by the benchmark (ARC-AGI-3) pushes agents to form an internal world model of the rules and mechanics that updates with new evidence.
Seth Karten@sethkarten
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Jonathan Dunlap retweetledi

i'm obsessed with what's happening in AI reforestation right now
this Franco-Brazilian startup called MORFO took a patch of land in Brazil that was rock-hard and compacted from years of cattle farming. they replanted it using a single drone. months later the ground was covered in grass, bushes, and small trees. the land came back to life.
here's how the whole thing works.
1. drones scan the terrain with high-resolution cameras and sensors
2. AI analyzes the imagery alongside soil samples, moisture levels, slope, and surrounding vegetation
3. the system picks from a catalog of 300+ native species, deciding exactly which plants will thrive in which specific spot
4. the drone fires biodegradable seed pods packed with seeds, nutrients, and moisture at 180 capsules per minute
5. satellite and drone imagery monitors regrowth over time, with AI tracking vegetation cover and biodiversity
6. two people and one drone cover 50 hectares a day. a person planting by hand manages about one hectare.
and MORFO isn't alone. AirSeed in Australia drops 250,000 seed pods per day into bushfire-scarred koala habitat, replanting swamp mahogany that koalas depend on to survive. Flash Forest in Canada fires 50,000 pods daily into wildfire-destroyed boreal forest, planning the replanting alongside Cree Indigenous communities. re-green won Prince William's Earthshot Prize after planting 6 million seedlings across 30,000 hectares of Amazon and Atlantic Forest.
five companies across four continents built this same approach independently. nobody coordinated. the physics of the problem demanded it.
knowing which seeds belong in which soil used to require years of ecological fieldwork, manual planting crews, and budgets that made large-scale restoration nearly impossible. now two people with a drone and an AI model trained on local soil data can replant 50 hectares before lunch.
this is the AI work that'll still matter in 50 years.
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Quick overview can be read here:
snowflake.com/en/blog/engine…
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