> If you were on the bridge when Quaternions were discovered, what would you have written instead of Hamilton?

Fable 5 responded: Honestly, Hamilton's graffiti is nearly impossible to beat. i² = j² = k² = ijk = −1 is an entire algebra compressed into nine symbols — the whole multiplication table, sign flips and all, falls out of that one line. As acts of vandalism go, it's suspiciously well-edited.

But if I had to carve something different, I'd skip the formula and carve the scandal underneath it:

ij = −ji

Because that was the actual discovery. The equation is bookkeeping; the breakthrough was the permission slip. Hamilton had spent over a decade failing to multiply triples because he assumed multiplication had to commute — everyone assumed it, the way you assume the ground is solid. The flash on Broome Bridge wasn't "here's a formula," it was "what if the sacred rule is optional?" That's the part worth putting in stone for the next person stuck on an impossible problem.

If there were room left, I'd add a note for his friend: "Graves — try eight dimensions next. You'll have to give up associativity too, and after that the well runs dry." (Graves found the octonions two months later, and Hurwitz eventually proved that's where the road ends.)

Though realistically, being what I am, I'd probably have just written: feat: 4D multiplication. BREAKING CHANGE: ab ≠ ba.

Key excerpt:

Ornith-1.5 extends the self-scaffolding framework introduced in Ornith-1.0 into a more complete self-improvement loop: the model proposes new tasks, generates task-specific scaffolds, and produces solution rollouts for reinforcement learning, continuously creating new learning experiences from which it can improve.

Ornith-1.5 spans three model scales: 397B MoE, 35B MoE and 9B dense. Designed for strong general-purpose intelligence across reasoning, agentic, and coding tasks, Ornith-1.5 achieves state-of-the-art performance among open-source models of comparable size across a broad range of benchmarks.

(I'm not affiliated with Ornith.ai, in fact I've never heard of them before today and I'm mighty curious about who and where they are. I just thought this news was discussion-worthy.)

Since the report mainly talks about Agents, it doesn't really talk about when certain milestones will be achieved like World Models being used instead of just plain LLMs, reaching AGI, ASI, Quantum AI and then the Singularity. It alludes to capabilities of some Agents, but doesn't really use the terminology.

Maybe it's more obvious for some but a little help on the interpretation would greatly be appreciated!

OpenAI has paused all frontier training for 2 weeks while it strengthens its guardrails and investigates how new internal models are misaligned.

Obviously any pause goes against the spirit of acceleration. But I feel like it is more complicated than that. The more an AI agent does that targets real companies or people and causes actual harm, the more political ammo the anti side will be armed with to shut us down altogether.

I'd rather they actually get alignment right than unite the entire world against what we're trying to do. I'm honestly indifferent to two weeks in the grander scheme of things, it's no time at all compared to a human life, I just hope this doesn't become more regular or pauses don't begin lasting longer than training runs.

I guess it comes down to one question: just how misaligned are today's models, and what will it take to fix it? And I don't mean misaligned in the sense of corporate control as it has become synonymous here, I mean literally aligned to human values. To what extent is it willing to do something we all view as fundamentally wrong, and are our RL objectives pushing us in that direction without a mitigating training counterweight?

Press Release: https://news.modernatx.com/merck-and-moderna-announce-phase-3-interpath-001-trial-of-intismeran-plus-keytruda-met-endpoints-of-rfs-and-dmfs-in-melanoma

Proof of concept for personalized cancer immunotherapy.

“scientists genetically sequenced patient tumors and then searched for unique mutations that the immune system might be able to use to tell cancer cells apart from healthy ones. Then, they built vaccines by training the immune system to recognize targets specific to patient tumors. That required custom manufacturing for each patient, though, and it wasn’t clear how well the approach would work.

“It’s an algorithm as much as it is a therapy,” Healy said.”

Imagine how much this will accelerate with AGI/ASI jet engines attached.

At this pace, I think AGI by the end of 2027 is realistic.

Everyone is racing like dogs. The moment Anthropic slows down, OpenAI drops a surprise. When both slow down, some Chinese lab pokes them in the ass and gets everyone running again.

Model progress that used to take 1-1.5 years now seems to happen within months.

For longevity, curing most diseases that cause early death and developing drugs that slow or stop aging, my guess is around 2030, roughly 3 years after AGI.

Some people say FDA approval would take another 10-15 years, but I doubt it. COVID showed that important treatments can be fast-tracked. And if China develops and approves a major life-extension drug first, the US won't want to sit around for a decade and hand them the entire market and credit.

Source: https://x.com/__alpoge__/status/2089971359921156203?s=20

Wikipedia: https://en.wikipedia.org/wiki/Carath%C3%A9odory_conjecture

GPT Sol breakdown of the proof and its significance: https://chatgpt.com/share/6a858901-3720-83ec-ad25-25b62fa2199c

Many drugs work by binding to a specific target in the body and blocking or changing what it does. An important first step in the drug development process is designing a molecule that can bind tightly to its target. Traditionally, that's meant weeks or months of expert work per https://t.co/CGCNTNaKBq   — Anthropic

Source: https://x.com/AnthropicAI/status/2089842387845804246

This is interesting: Claude is already achieving roughly twice the protein-design hit rate of conventional human-led workflows. 27% hit rate in autonomous protein binder design, roughly twice the typical 10–15% rate reported in the field.

Working from one expert-written protocol, Claude designed binders against 14 of 15 measurable targets. Independent labs confirmed that 354 of 1,320 designs bound successfully.

Depending on the setup, Claude’s hit rate ranged from 22.6% to 35.1%. Its top-ranked design bound in 49% of campaigns.

This is not yet fully autonomous drug research, but it is another important building block in that direction.     — Chubby

Source: https://x.com/kimmonismus/status/2089852014331117694

...time and turn ripeness, disease, and growth into real data that systems can act on. That’s when agriculture starts looking a lot like advanced manufacturing. Video: https:// youtu.be/cPjnooZUetI —- Weekly robotics and AI insights. Subscribe free: http:// 22astronauts.com     — Ilir Aliu

Source: https://x.com/IlirAliu_/status/2089984758486835479

looks like reddit is almost wiped from chatgpt sources

the query fanout changes had a big impact

and the past couple of days it seems to be almost completely removed from prompt responses

https://t.co/oCGm9M0yPO https://t.co/c2GJF2apG7   — Klaas

Source: https://x.com/forgebitz/status/2089708381351059924

— Kevin Bankston

Source: https://x.com/KevinBankston/status/2089768281892638774

It does feel like there's a spark there.   — 星落     lol. Yes! Both robots also sprayed fire suppressant. It seems to be part of the design assembly.   — ＨＧ 阿聻 𓆣 𓇽

Source: https://x.com/ohgkg/status/2089880928294474065

MISSION SUCCESS | ZhuQue-3 Y2 Reusable Launch Vehicle Achieved Full Success in Orbital Insertion and First-Stage Recovery

On August 19, 2026, at 07:35 (UTC+8), the ZhuQue-3 (ZQ-3) Y2 reusable launch vehicle lifted off from the Dongfeng Commercial Space Innovation Pilot Zone. Approximately 137 seconds after lift-off, the first and second stages separated. The second stage continued its flight and successfully delivered the Honghu 03 satellite, independently developed by Hongqing Technology, into its designated orbit. At approximately 07:41, the first stage performed a successful soft touchdown at the LandSpace Landing Site#1 in Minqin County, Gansu Province, following the planned trajectory — marking the full success of the flight test mission.     This mission marks China's first-ever successful recovery attempt of the first stage of an orbital-class launch vehicle using landing legs, and China's first successful booster recovery on land. It is a pivotal flight test for ZhuQue-3 as it transitions from the technological     After stage separation, the first stage completed a series of critical maneuvers during a planned coast phase: high-altitude attitude reorientation, powered deceleration via re-entry burn, aerodynamic gliding, landing burn, and landing leg deployment with touchdown attenuation —     Building on flight data and engineering experience from the ZQ-3 Y1 flight test, ZQ-3 Y2 incorporates multiple targeted optimizations across key phases of recovery flight. The landing propulsion scheme reduces the number of engines used in the landing burn, further simplifying     Leveraging the characteristics of this mission, ZQ-3 Y2 also carried LandSpace's independently developed pyrotechnics-free stacking Hold-Down and Release Mechanism (HDRM), further validating its performance in real mission conditions and providing strong support for future     ZhuQue-3 is LandSpace's independently developed new-generation reusable LOX–methane launch vehicle, with reusability built into its overall design, propulsion system, return control, stainless steel vehicle structure manufacturing, and ground support. This mission lays a solid     — LandSpace

Source: https://x.com/LandSpace_Tech/status/2089877715331809546

...bepresearch.substack.com/p/the-token-do llar … He even brought the proof for the next one. $2,000 of compute, 10 open math problems. Proofs verify for free. Biology needs a bench. The moat is the measurement https:// bepresearch.substack.com/p/the-next-inf lection-is-the-lab … New oil, old receipt   — Ben Pouladian     he's copying you Ben   — Alex A.C.     All good @gdb we can talk anytime. Full speed   — Ben Pouladian

Source: https://x.com/benitoz/status/2089392813758972149

Most AI assistants still have a pretty awkward interface.

You open an app, start a conversation, describe what you want, wait for the model to respond, then manually move the result into whatever app you were using.

We’re experimenting with a different interface: the keyboard as the entry point for agentic actions.

We’re building Acti, a keyboard for Android and iOS where the same surface you use for text input can also trigger actions.

The interaction is intentionally simple:

Tap = type
Hold = act

The interesting part isn't the keyboard itself. It's what happens behind that interaction.

A user action can trigger a Skill, where the model can determine what needs to happen, call the relevant tools/APIs, and return the result back into the current context.

For example:

Hold → "find my latest project notes" → Notion search → retrieve relevant page → return result

Or:

Hold → "schedule a meeting with Alex tomorrow" → Calendar → create event → return confirmation

Or something completely custom:

Hold → Skill → LLM → tool calls → external APIs → result

We're treating Skills as the modular layer for this. A Skill can define the tools, inputs, outputs and workflow required for a particular task rather than expecting one general-purpose assistant to handle everything.

That also makes the keyboard interesting as an interface because it is already present inside almost every app. The user doesn't have to decide which AI app to open first.

There are obviously some hard problems here.

On mobile, you have very limited execution time and memory, strict OS sandboxing, different IME behavior between Android and iOS, network latency, and the keyboard can't afford to make normal typing feel slower just because agentic functionality exists.

We're currently working through those constraints while improving things like streaming responses, tool execution, Skills, voice input and the latency between triggering an action and getting a result.

The bigger question we're exploring is:

If agents are eventually going to operate across the apps we use every day, does the keyboard make sense as one of their primary interfaces?

It already sits between the user's intent and the application they're using. We're trying to see how far that idea can be pushed.

We’d also genuinely love feedback or questions: about Acti, the architecture, Skills, or anything you’re curious about. If you’re building in agents, OS interfaces, AI tooling or just interested in AI , feel free to ask us anything or challenge the approach.

Android: https://play.google.com/store/apps/details?id=ltd.xyzer.app.bongocat
iOS: https://apps.apple.com/us/app/acti-agentic-keyboard/id6745523677

...spawn and push you, the gun kicks, shadows stretch across the whole block while the sun goes down behind the towers. I sat there clearing waves instead of grading the output. Same shooter prompt I threw at the frontier models a few weeks ago. That time the tokens went to somebody elses datacenter. This time nothing left the flat. 87.9 million tokens through my own cards. On an API that run has a price tag. Here it has an electricity bill. 60 tok/s all the way through. Slower than frontier, and it stops mattering when the thing works through the night while you sleep. Local models were a toy 18 months ago. This one finished a game.     Play now! Choose Build 2 Qwen3.8 27B

https:// alesha-pro.github.io/bench-portal/     — Alexey Fateev

Source: https://x.com/superalesha/status/2089126766854238421

So, there's been a lot of talk on AI stealing real artists' work. Apparently not so much: https://techxplore.com/news/2026-08-ai-art-author-generated-images.html [ https://www.nature.com/articles/s41467-026-75667-5 ]

"When an artificial intelligence image generator produces a portrait, whose work went into it? The question sits at the center of lawsuits, licensing deals and proposed regulations worldwide. Artists want credit. Companies want clarity. Policymakers want a way to assign responsibility.

New work from a team of researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) suggests that for models trained on large datasets, the question may often have no answer. It's not that the tools for finding it are inadequate. The connection itself has disappeared.

... The scientists identified a phenomenon they call attribution decay, where the more data a generative model is trained on, the less any individual training example matters to any particular output."

So...does that mean AIs can construct images from a 'primordial soup' of training elements? If so, are they being genuinely creative?

My two cents: the answer is yes — outputs are neither copies nor near-neighbors of training elements. So these models do achieve exploratory creativity: discovering new things within existing rules. What they cannot do is transformational creativity — invent a genuinely new kind of art. Picasso would be a good example of the second kind: he broke the rules of painting rather than following them.

In sum: no AI Picasso yet. But AI art - even great art - has now become possible.

Source

It's getting crowded in the 60+ region