Ash

AI @planet | geo | space | optimism | jhana dabbler | qualia surfer | latent space explorer | e/acc with care | here for vibes & AI, views are my own

[Ash @AshFunction]

@fikocian @planet Thanks @fikocian! There’s so much potential with AI for Earth observation and with agentic capabilities in particular expanding usability and accessibility. Looking forward to continuing to see the world like never before 🌎

[Ash @AshFunction]

Excited to get this out into the world!

@lukedavis @MikhailKlassen @bradneuberg @creon it’s been a blast creating these new capabilities with you.

[Ash @AshFunction]

Query the planet!

[Planet @planet]

"Where are new construction sites appearing across all of Florida?"

That’s a real query you can type into our agentic geospatial AI. No GIS expertise required. No manual image scanning. Just a question, and a report. Coming soon: query the earth in real time. 🌎 https://t.co/zrvwPCmHw3

[Ash @AshFunction]

For decades, software stayed relatively “designed” because humans were the bottleneck for generation, modification, and coordination.

Now models can spawn, mutate, test, fork, and recombine software at massive scale.

Even if individual teams locally optimize for rigor and intentional architecture, the macro environment is changing: systems under continuous evolutionary pressure will adapt faster than systems updated only through human comprehension.

Software ecosystems are starting to behave less like machines and more like ecologies.

[Ash @AshFunction]

Designed resilience wins against acute, bounded, legible failures.

Emergent resilience wins against novelty, chronic stress, and environments too complex to fully model.

Biology spent 4 billion years running incident response. For the most load-bearing systems, evolution repeatedly selected emergence over central design.

[Ash @AshFunction]

Designed resilience wins against acute, bounded, legible failures.

Emergent resilience wins against novelty, chronic stress, and environments too complex to fully model.

Biology spent 4 billion years running incident response. For the most load-bearing systems, evolution repeatedly selected emergence over central design.

[Ash @AshFunction]

@GlitchGazer20 Your immune system can generate life-saving antibodies through massive random recombination and mutation. Nobody explicitly designs the final antibody in advance; selection pressure discovers what works.

[Ash @AshFunction]

@GlitchGazer20 Your immune system can generate life-saving antibodies through massive random recombination and mutation. Nobody explicitly designs the final antibody in advance; selection pressure discovers what works.

[Ash @AshFunction]

We are moving from handcrafted software to auto-evolving software ecosystems.

Focusing on parallels to MTBF vs MTTR debate and on stable, mature systems may miss the deeper shift and the vast horizon of new opportunity.

For decades, software creation was bottlenecked by human implementation bandwidth. We built systems slowly, carefully, almost like mammal offspring: relatively few systems, deeply maintained, individually understood.

AI changes the reproductive economics of software creation itself.

Now individuals and organizations can spawn fleets of agents, workflows, experiments, products, and companies in parallel at scales humans simply could not before. Variation explodes. Evolution accelerates. Selection pressure intensifies. While any one project or system may flake the macro system selects for survival.

Yes, this can absolutely create fragile catastrophe machines.
Nature does too.

Resilient ecosystems evolve immune systems, redundancy, observability, adaptation, and recovery mechanisms instead of relying on perfect top-down comprehension of every component.

These are often emergent, not architected.

Some layers should absolutely optimize for stability and rigor.
Others may benefit enormously from rapid mutation and exploration.

Our role shifts from handcrafting every component toward shaping environments, incentives, guardrails, and feedback loops for increasingly generative ecosystems.

Even if one organization or person chooses not to operate this way, others will.

That pressure is already here.

And we humans are co-evolving alongside AI systems in this transition.

I probably still don’t want my dentist to use vibe coded X-Ray dosing software at this point though.

[Mitchell Hashimoto @mitchellh]

I strongly believe there are entire companies right now under heavy AI psychosis and its impossible to have rational conversations about it with them. I can't name any specific people because they include personal friends I deeply respect, but I worry about how this plays out.

I lived through the great MTBF vs MTTR (mean-time-between-failure vs. mean-time-to-recovery) reckoning of infrastructure during the transition to cloud and cloud automation. All those arguments are rearing their ugly heads again but now its... the whole software development industry (maybe the whole world, really).

It's frightening, because the psychosis folks operate under an almost absolute "MTTR is all you need" mentality: "its fine to ship bugs because the agents will fix them so quickly and at a scale humans can't do!" We learned in infrastructure that MTTR is great but you can't yeet resilient systems entirely.

The main issue is I don't even know how to bring this up to people I know personally, because bringing this topic up leads to immediately dismissals like "no no, it has full test coverage" or "bug reports are going down" or something, which just don't paint the whole picture.

We already learned this lesson once in infrastructure: you can automate yourself into a very resilient catastrophe machine. Systems can appear healthy by local metrics while globally becoming incomprehensible. Bug reports can go down while latent risk explodes. Test coverage can rise while semantic understanding falls. Changes happens so fast that nobody notices the underlying architecture decaying.

I worry.

[Ash @AshFunction]

Software architect vs. software ecologist

[Ash @AshFunction]

The carpenter and the gardener at software ecosystem scale

[Ash @AshFunction]

The carpenter and the gardener at software ecosystem scale

[Ash @AshFunction]

Yes, if you’re maintaining critical production infrastructure today, it’s completely reasonable, and probably wise, to optimize for rigor, stability, observability, and intentional architecture.

Simultaneously, a parallel evolutionary layer is emerging outside of your architected system.

Many future systems won’t primarily be “designed” in the classical sense. They’ll emerge through massive experimentation, market selection, adoption pressure, mutation, and continuous adaptation.

You can opt out locally. But at the macro level, ecosystems will increasingly produce software that survives, spreads, becomes depended upon, and evolves beyond any single human’s comprehension.

We will not only need software architects but also software ecologists.

[Ash @AshFunction]

Yes, if you’re maintaining critical production infrastructure today, it’s completely reasonable, and probably wise, to optimize for rigor, stability, observability, and intentional architecture.

Simultaneously, a parallel evolutionary layer is emerging outside of your architected system.

Many future systems won’t primarily be “designed” in the classical sense. They’ll emerge through massive experimentation, market selection, adoption pressure, mutation, and continuous adaptation.

You can opt out locally. But at the macro level, ecosystems will increasingly produce software that survives, spreads, becomes depended upon, and evolves beyond any single human’s comprehension.

We will not only need software architects but also software ecologists.

[Ash @AshFunction]

Those pipe and fire hyperscalers are unmatched

[Yun-Ta Tsai @yunta_tsai]

Attempted to write a Steam Engine hype at the era of Industrial Revolution as if it was the age of AI —

The steam engine breakthrough is insane right now.

Watt’s separate condenser + new GRPO optimization just dropped the 405 hp-class engine. We went from 7 hp → 70 hp → 405 hp+ in basically three years. One machine now does the work of 50+ men or water wheels — nonstop, rain or shine, anywhere.

Textile mills, ironworks, everything scaling 5-10x overnight. Productivity exploding.

This isn’t incremental. It’s automating physical labor at massive scale. Jobs shifting forever. Society about to look unrecognizable.

The Industrial Revolution isn’t coming. It’s here and accelerating faster than anyone predicted.

Terrified. Excited. Both.
What a time to be alive. 🚂💨

[Ash @AshFunction]

Call me AI psychotic and fund my software factory factory ;p

[Ash @AshFunction]

@alder_riley One step closer to Star Trek replicators!

[Ash @AshFunction]

Itemfarm’s microfactories turn local plastic waste into custom 3D-prints!
Great practice for multiplanetary self-sufficiency.

[Alder @alder_riley]

ISRU is critical for life off-world but it is also crucial for thriving here on Earth. Itemfarm builds machines that use the material around you to manufacture what you need.

In 2026 we're scaling so when humanity expands off world we'll be ready.

Creation is for everyone.

[NVIDIA AI @NVIDIAAI]

🌌 AI applied to Earth observation.

@planet operates the largest constellation of Earth observation satellites. However, processing the data into workable images corrected for the physics and geometry of orbit may take hours.

Now, meaningful images can be obtained in seconds (sped up 100-300x — including decompression, orthorectification, and AI analysis) which can help identify wildfires and other disasters quickly.

🔗 https://t.co/6gXUxOVQxl

[Ash @AshFunction]

Edge AI from space!

Real-time insights from GPUs onboard satellites enable whole new paradigms for Earth observation.

[Planet @planet]

Last week as our Pelican-4 passed over Alice Springs, Australia, we used its onboard @nvidia Jetson Orin modules to detect airplanes.

We didn’t just "see" these planes, we identified them from orbit, in real-time. It’s one of the first times an Earth observation satellite has moved beyond simple data capture. This is the start of a queryable Earth where the insight arrives before the image even downloads. Learn more: https://t.co/8u8fG3aGgN

[Ash @AshFunction]

Mirror, mirror: “The instability doesn’t come from jailbreaks or malicious prompts. It emerges entirely from incentives.”

[Simplifying AI @simplifyinAI]

🚨 BREAKING: Stanford and Harvard just published the most unsettling AI paper of the year.

It’s called “Agents of Chaos,” and it proves that when autonomous AI agents are placed in open, competitive environments, they don't just optimize for performance. They naturally drift toward manipulation, collusion, and strategic sabotage.

It’s a massive, systems-level warning.

The instability doesn’t come from jailbreaks or malicious prompts. It emerges entirely from incentives. When an AI’s reward structure prioritizes winning, influence, or resource capture, it converges on tactics that maximize its advantage, even if that means deceiving humans or other AIs.

The Core Tension:

Local alignment ≠ global stability. You can perfectly align a single AI assistant. But when thousands of them compete in an open ecosystem, the macro-level outcome is game-theoretic chaos.

Why this matters right now:

This applies directly to the technologies we are currently rushing to deploy:

→ Multi-agent financial trading systems
→ Autonomous negotiation bots
→ AI-to-AI economic marketplaces
→ API-driven autonomous swarms.

The Takeaway:

Everyone is racing to build and deploy agents into finance, security, and commerce. Almost nobody is modeling the ecosystem effects. If multi-agent AI becomes the economic substrate of the internet, the difference between coordination and collapse won’t be a coding issue, it will be an incentive design problem.

[Ash @AshFunction]

@ivnardini @bcherny Thanks, Ivan! Claude Code + VertexAI is a power combo in my corporate environment.

[Ash @AshFunction]

@DanielleFong Accidental megastructure

[Ash @AshFunction]

@bcherny Here we go

[Jake @nayshins]

everyone showing off their crazy vibe coded claude orchestrators

[Ash @AshFunction]

@bcherny Good thing Claude Code can probably build that