(Pronounced Clarity) The cure for agent amnesia. Persistent Mamba inference that cuts tokens, compute & energy without touching output quality. - Billy Brenner

Los Angeles, CA
Joined March 2026
Clarit.AI retweeted
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel. - Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all. - Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract. - Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code. - And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while. The funnel is driven by a combination of factors: 1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel. 2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel. 3. Access. Free-tier, paid-tier, internal. So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code. But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along. So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions. TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
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Eager for v1.4.0 release of @TraycerAI ! It's become my go-to ADE for all projects.
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Well I may have burnt through all 11,000,000,000 tokens in 48 hours but I'm really digging the @XiaomiMiMo token plan! $15 for this is an insane value. Mimo-2.6-Pro is a workhorse! Time to upgrade tiers ;)
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So apparently @TDBank_US thinks @deepseek_ai is a fraudulent company and is cancelling my card over a $20 reload that I authorized. What a farse! Do better TD Bank
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I really hope @thsottiaux has a physical reset button on his desk. Like a big red button built off an esp32 that triggers the reset, launches a automation macro with flashing lights and ish 😂
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Hey @TraycerAI any plans on adding @antigravity support? It would likely have to be TUI/CLI only to adhere to their terms, not wrapped in your GUI, which is why Spotify XIRP is able to do it (and the AGY team publically offered to help Spotify upgrade their product from Gemini CLI to AGY)
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Idea for @bcherny / @trq212: when a peer session messages a Claude Code agent, don't inject it at the next tool boundary. Fork a /btw-style side session (cached, no tools) to triage it and answer from context if it can, otherwise hand the primary a one-liner at end of turn. A receptionist, not an interrupt.
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More detailed: Cross-session messaging in Claude Code is great, but incoming agent messages land mid-turn at the next tool boundary, same as a user interrupt. They shouldn't get that priority. Proposal: route inbound peer messages through a /btw-style fork of the primary's context. It reuses the prompt cache so it's nearly free. The fork can answer "what did you change?" from what it already knows, or reply "primary is mid-refactor, will circle back," and hand the main agent a one-line digest at end of turn instead of derailing it. Basically leverage existing functionality to create a receptionist function that prevents the primary agent from being interrupted and redirected in the middle of existing work, and give the receptionist a way to interrupt on purpose when something's actually urgent.
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Keep going family!!
As a bit of a Google Trends nerd 😂 If Stargate were a dead franchise and aimed at a “small dedicated fanbase” you wouldn’t expect worldwide search spikes for “Stargate SG-1 Netflix” (+800%), “Stargate Netflix,” and “Stargate new series.” The datas suggestion is there is still a strong meaningful audience demand whenever the franchise becomes visible on a major streaming platform for example. (There’s numerous examples of this kind’ve trend) So if little old me with a PC and a pretty ok understanding of how social media ticks can see the missed goal here. How is it the executive at Amazon MGM getting paid what I’m sure is top money get to the decision they did from a strategic standpoint? I feel there’s a missing piece here because it makes no sense. lol. #stargate
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Hey Stargate Fans please let @amazon and @AmazonMGMStudio know how you feel about them cancelling #Stargate They MOCK us. They think they don't need us. Let them know what you think of that. I won't be watching ANY other Stargate that is created without the OG creators /Gero being involved. I'm DONE with studios SHITTING ON THE FANS AND KILLING IPS WE LOVE. #stargate #stargatecanceled #amazon #amazonstudios #amazonmgmstudio
HEY @amazon @AmazonMGMStudio I REFUSE to believe that the original creators of #Stargate would have created something that wouldn't have had broad appeal. They are extremely talented people that created one of the biggest franchises in our time. REVERSE this decision! LET THEM SHOW YOU AND US WHAT THEY CREATED. Look at what is happening to Disney Star Wars at the box office right now. Look at how the new Star Trek Academy went. WHY do you keep making the same mistakes?!? IT'S NOT TOO LATE. LET GERO, JOE AND THE OG'S COOK!
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I’m gonna simply say this: if you are at all interested in a Stargate show with ANY of the original creators/performers involved, now is the time to say something. Otherwise it really will be the end of that chapter forever. Let them know you are THERE
Hey Stargate Fans please let @amazon and @AmazonMGMStudio know how you feel about them cancelling #Stargate They MOCK us. They think they don't need us. Let them know what you think of that. I won't be watching ANY other Stargate that is created without the OG creators /Gero being involved. I'm DONE with studios SHITTING ON THE FANS AND KILLING IPS WE LOVE. #stargate #stargatecanceled #amazon #amazonstudios #amazonmgmstudio
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Shai-Hulud malware in PyTorch Lightning. Every team training on a third-party stack just inherited the blast radius. The fewer pip installs between you and the model, the smaller your supply chain. Inference infra is no different.
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Loading a snapshot to disk. Putting it back in memory. Returning 200 OK. None of that proves the model consumed it. The only proof is the next token coming out different than baseline. Restore is not the same as use.
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Engram now validated for multi-tenant memory isolation. Each session restores its own memory. No bleed across tenants. v0.5.2 shipped. The cure for agent amnesia, now safe at scale.
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Someone give @sama a reason to reset codex limits today, we got more work to do! 😜
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Okay y'all, which agent am I working with? "This one's on me, and I want to own it cleanly."
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Mamba-3 ships inference-first. Compact memory. Lower decode latency. Now imagine that memory surviving the session. Save it. Restore in 2ms. Skip the rehash. Engram is what makes inference-first actually persistent.
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Everyone's watching @GoogleAI wondering why they haven't released a new model recently, but they haven't been idle, they've been integrating Gemini into every product at various layers. When the new model(s) drop, the integration layer is already prepped, validated & waiting. – at Los Angeles, CA
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Any LA developers working with @nvidia Jetson Thor and open to collaborating on stateful Mamba/SSM inference? cc @NVIDIAAIDev – at Los Angeles, CA
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