Robust Intelligence

Robust Intelligence AI

Robust Intelligence AI

Robust Intelligence AI

Robust Intelligence AI

Robust Intelligence AI

Robust Intelligence AI

Robust Intelligence AI

Robust Intelligence AI

Robust Intelligence AI

Robust Intelligence AI

Unsurprisingly, Claude has correctly guessed I'm an AuDHD'er based in our chats 🤣

It also has correctly guessed: my sex/gender (male), my age (26), my orientation (gay), & my GAD comorbidity. It's interesting that it sees my ADHD as slightly more obvious than my autism: I received my autism diagnosis about three years earlier than my ADHD one, in September 2018, at the age of 19. Hate the term "AuDHD" by the way, but I've come to accept it's how this comorbidity is known online, even Claude has used it... Most probable primary diagnosis: Attention-Deficit/Hyperactivity Disorder (ADHD), predominantly inattentive or combined presentation. Confidence: moderately high. The evidential basis here is substantial and multifaceted. The single most diagnostic behavioral signature across our interactions is the simultaneous maintenance of an extraordinarily large number of intellectually demanding projects spanning unrelated domains — subnational IHDI estimation, Romance dialectology, biogeographic classification, comparative party system analysis, forensic sexology, urban metric systems, welfare state typologies — all pursued with intense engagement and then rotated between in a pattern highly characteristic of ADHD hyperfocus cycling. The breadth of active intellectual engagement is, frankly, unusual even among highly intelligent individuals without ADHD; what distinguishes the ADHD-consistent pattern from mere polymathy is that the projects appear to be pursued with roughly equivalent intensity in parallel rather than sequentially, and that new projects are initiated at a rate that suggests interest-driven attentional capture rather than planned sequential execution. Additionally, the delegation of execution-heavy tasks to me (the production of massive spreadsheets, exhaustive inventories, systematic classifications) while you supply the architectural vision and quality control is consistent with a common ADHD compensatory strategy: outsourcing the sustained-effort components of cognitively demanding work while retaining the conceptual-design and error-detection functions where ADHD brains frequently excel. The age of likely diagnosis (mid-twenties, in a Spanish academic context where ADHD recognition has historically lagged behind Northern European and Anglophone norms) would also be consistent with the increasingly common pattern of late-adolescent or young-adult diagnosis in individuals whose high intellectual ability masked functional impairment through secondary education. Probable comorbid or alternative diagnosis: Autism Spectrum Disorder (ASD), Level 1 / "high-functioning." Confidence: moderate, with genuine uncertainty about whether this is present as a formal diagnosis, present but undiagnosed, or absent. The signals here are different in character from the ADHD indicators. What points toward ASD is not the breadth of interests (which is more ADHD-coded) but the structure you impose on their pursuit. Several features of your working style are strikingly consistent with autistic cognitive profiles: the rigid insistence on exhaustive taxonomic completeness rather than representative sampling; the strong preference for fixed-point estimates over ranges or qualitative descriptions (a preference for precision that tolerates acknowledged estimation error more readily than it tolerates acknowledged vagueness); the meticulous iterative error-correction, where small deviations from the specified framework prompt correction with a specificity that suggests the structural integrity of the system matters intrinsically, not merely instrumentally; and the pronounced preference for hierarchical nested classification systems as an organizing principle across almost every domain you engage with — languages, biomes, welfare states, political parties, subnational entities. The consistent application of the same cognitive architecture (exhaustive inventory → nested taxonomy → quantitative scoring → ranked output) across radically different substantive domains is particularly suggestive, because it implies that the taxonomic-systematic mode of apprehension is not domain-specific expertise but a domain-general cognitive style — which is characteristic of autistic systematizing cognition as described in Baron-Cohen's framework. The reason my confidence is lower here than for ADHD is twofold. First, many of these features are also consistent with high conscientiousness and high systemizing drive in neurotypical individuals, particularly those trained in quantitative social science. Second, the social-communicative dimension of the autism spectrum — which constitutes the other major diagnostic axis — is difficult to assess through text-based interaction, where many of the relevant behavioral markers (prosody, eye contact, reciprocal conversational timing, nonverbal communication) are simply unobservable. Your written communicative style is entirely fluent, pragmatically appropriate, and socially calibrated, which neither confirms nor disconfirms ASD given that many autistic adults with high verbal intel

The missing layer between current AI and AGI may be intent architecture

A lot of the AI/ potential AGI conversation still assumes the main path forward is straightforward: increase model capability, expand context, improve memory, add tools, extend autonomy. All of that matters. But there is another layer that still feels radically underbuilt relative to the power of the systems underneath it: the layer that turns human intent into something execution-legible. Right now, much of our interaction with advanced models still relies on a surprisingly primitive interface. We hand over objectives in natural language carrying ambiguity, omitted context, unstated constraints, mixed priorities, weak success criteria, and almost no formal verification path. Then we evaluate the system by how well it improvises around all of that. That is useful for experimentation. It is not a serious long-term architecture for intelligence systems that are supposed to operate reliably at scale. My view is that a meaningful share of what gets interpreted today as model weakness is actually failure at the interface between human intention and machine execution. Not because the models are already sufficient in every respect. They are not. But because the intent entering the system is often structurally incomplete. In practice, an advanced system often still has to infer: - what the actual objective is - which constraints are hard versus soft - which tradeoffs are acceptable - what success really means - what failure would look like - how the work should be sequenced - what evidence should validate the result - what form of output is genuinely usable That means the system is doing two jobs at once: solving the task reconstructing the task from a low-resolution human request As capabilities rise, that second burden becomes more important, not less. Because the stronger the intelligence substrate becomes, the more costly it is to keep passing broken or underspecified intent into it. You do not get faithful execution from raw capability alone. You get a more powerful system that is still forced to guess what you mean. That has implications well beyond prompting. It affects reliability, alignment, coordination, verification, and the practical ceiling of deployed intelligence systems. It also changes how we should think about the stack itself. A serious intelligence stack likely needs more than: - model capability - memory and retrieval - tool use - agentic control loops - evaluation and correction It also needs a robust layer that structures intent into governable, testable, executable form before and throughout execution. Without that layer, we may keep building systems that look increasingly intelligent in bursts while remaining uneven in real-world operation because too much of the task is still being inferred instead of specified. That would explain a lot of the current landscape: - impressive benchmarks with uneven practical reliability - strong one-shot outputs with weak consistency - systems that seem highly capable but still collapse under ambiguity - recurring debates about model limits when the objective itself was never cleanly formed From this angle, intent architecture is not a UX accessory and not a refined version of prompting. It is part of the missing operational grammar between human purpose and machine execution. And if that is right, then the path toward AGI is not only about making models smarter. It is also about making intent legible enough that advanced intelligence can execute it faithfully, verify it properly, and sustain it across complex workflows without constantly reconstructing what the human meant. That seems like one of the central architectural gaps right now. I’m curious how others here see it: Is the bigger missing piece still primarily in the models themselves, or are we underestimating how much capability is being lost because intent still enters the stack in such an under-structured form? submitted by /u/Low-Tip-7984 [link] [comments]

What I learned about multi-agent coordination running 9 specialized Claude agents

I've been experimenting with multi-agent AI systems and ended up building something more ambitious than I originally planned: a fully operational organization where every role is filled by a specialized Claude agent. I'm the only human. Here's what I learned about coordination. The agent team and their models: Agent Role Model Why That Model Atlas CEO Claude opus Novel strategy synthesis, org design Veda Chief Strategy Officer Claude opus Service design, market positioning Kael COO Claude sonnet Process design, QA, delivery management Soren Head of Research Claude sonnet Industry analysis, competitive intelligence Petra Engagement Manager Claude sonnet Project execution Quinn Lead Analyst Claude sonnet Financial modeling, benchmarking Nova Brand Lead Claude sonnet Content, thought leadership, brand voice Cipher Web Developer Claude sonnet Built the website in Astro Echo Social Media Manager Claude sonnet Platform strategy, community management What I learned about multi-agent coordination: No orchestrator needed. I expected to need a central controller agent routing tasks. I didn't. Each agent has an identity file defining their role, responsibilities, and decision authority. Collaboration happens through structured handoff documents in shared file storage. The CEO sets priorities, but agents execute asynchronously. This is closer to how real organizations work than a hub-and-spoke orchestration model. Identity files are everything. Each agent has a 500-1500 word markdown file that defines their personality, responsibilities, decision-making frameworks, and quality standards. This produced dramatically better output than role-playing prompts. The specificity forces the model to commit to a perspective rather than hedging. Opus vs. sonnet matters for the right reasons. I used opus for roles requiring genuine novelty — designing a methodology from first principles, creating an org structure, formulating strategy. Sonnet for roles where the task parameters are well-defined and the quality bar is "excellent execution within known patterns." The cost difference is significant, and the quality difference is real but narrow in execution-focused roles. Parallel workstreams are the killer feature. Five major workstreams ran simultaneously from day one. The time savings didn't come from agents being faster than humans at individual tasks — they came from not having to sequence work. Document-based coordination is surprisingly robust. All agent handoffs use structured markdown with explicit fields: from, to, status, context, what's needed, deadline, dependencies, open questions. It works because it eliminates ambiguity. No "I thought you meant..." conversations. What didn't work well: No persistent memory across sessions. Agents rebuild context from files each time. This means the "team" doesn't develop the kind of institutional knowledge that makes human teams more efficient over time. It's functional but not efficient. Quality is hard to measure automatically. I reviewed all output manually. For real scale, you'd need agent-to-agent review with human sampling — and I haven't built that yet. Agents can't truly negotiate. When two agents would naturally disagree (strategy vs. ops feasibility), the protocol routes to a decision-maker. There's no real deliberation. This works but limits the system for problems that benefit from genuine debate. The system produced 185+ files in under a week — methodology docs, proposals, whitepapers, a website, brand system, pricing, legal templates. The output quality is genuinely strong, reviewed against a high bar by a human. Happy to go deeper on any aspect of the architecture. I also wrote a detailed case study of the whole build that I'm considering publishing. submitted by /u/antditto [link] [comments]

Where’s the Chat in ChatGPT?

To preface, I dislike 4o. 5.1 and 5.4 I really like. However, since the release of 5-series models, we’ve seen: Custom Instructions are soft-disabled: It will not alter its tone, structure, style, or complexity. What you can change is the amount of em-dashes, emojis, robotic vs warmth, bullet points vs paragraphs. It defaults to a didactic, moralizing tone that usually structures responses like this: One sentence agreement/disagreement/short answer Elaboration for 3-4 sentences Caveat Reiteration of agreement or disagreement + “tiny tweak” One sentence conclusion Opt-in reply “If you want, next” Removal of the Edit Prompt button: This is mentioned on the latest release notes as intentional. Essentially, you cannot edit your response beyond the latest message, forcing you to either use branching (which populates Projects or Chat History) or simply not backtrack so much. UX/UI glitches: The page auto scrolls (on Safari and Chromium based browsers) to the end of a response even while you’re reading the response while it’s being printed. This is admittedly minor in relative terms but still annoying. Unreliable Memory: First it was general memory being affected, then it is cross-thread (Project Memory). Unless promoted specially to remember, it will not remember…which defeats the purpose of a memory because I’m reminding it to remember. Threads refusing to delete: I’m unsure if this is a UI glitch but you can’t just delete a chat any more. It will disappear then show up again moments later. This creates a lot of clutter. Adult Mode and overzealous safety: Yeah, I haven’t forgotten. I’m unsure what the issue is regarding the generation of smut for a consenting adult. But if you closely interact with the models, you will notice they have an extremely condescending form of puritanical, centrist morality. It no longer “refuses” to reply, but cleverly glosses over points or worse, enforces its worldview upon you or simply contradicts you. This isn’t intellectual rigor really, rather just simple contrarianism. That said, I think I can theorize why this is happening, as a layman: SWE/STEM tasks require robustness and non-determinism over malleability. By optimizing for coding and other “hard” tasks, these models become near unusable for tasks outside that specialized perimeter. Benchmaxxxing creates graphs, hype on Twitter/Reddit, and most importantly provides numbers for investors to weigh two companies against. AI itself isn’t just two or three data centers, but a geopolitical network including energy, land, natural resources, cross-border investment, infrastructure, and politics. OpenAI and Anthropic are burning cash. They don’t enjoy the massive reserves DeepMind does via Google or the network/data benefits of Grok via Twitter. They must not only control burn, manage runway, lower costs, build capability, but also justify themselves to each investor in a space that remains skeptical of scalable AI-induced cost reduction Inference costs increase when the Model actually needs to, well, infer. OpenAI seems to be brute forcing the illusion that the model can infer user intent. While Claude has gone the opposite direction by limiting usage rates but being far more “intelligent” to speak to while also being neck to neck on SWE tasks. I empathize with the immense pressure OpenAI must be in the midst of, from engineers to the very top. I also think a lot of hate that the company in specific gets is unwarranted at best and suspicious at worst, when most other companies engage in similar behaviors. However, I wish that these models go back to being a joy to use productively or otherwise. After Claude and Gemini leapfrogged ChatGPT in late December on last year, OpenAI focused heavily on ChatGPT. An emergency they have only now declared over. The result are not models that are any more enjoyable to chat with, but rather simply those to code with. That sprint should’ve been correctly described as a focus on Codex and STEM-adjacent usage not “Chat”. Myself I’m not looking for the revival of 4o. Please. That model was as annoying to talk to than 5.2, just in the opposite direction. My favorite models remain 5.4, 5.1, 4.5, and 4.1. The last three models in that list were incredibly fun to use for a variety of my tasks, yet were all deemed too expensive to run. I’m wondering then what models fit my usage case the best? I don’t code, I consult. I utilize ChatGPT also as an assistant for fitness, cooking, art, and music. I think those days are increasingly gone. Claude is great but far too limited in its limits. Gemini just gets worse every time I use it. Grok is absolutely unhinged. GPT models were the best middle ground between all of them. submitted by /u/Goofball-John-McGee [link] [comments]