Every time at a big conference, it's been frustrating figuring out who else is in town... but just about solved it now: /asimov Who in my X network plus @ASIMOV_Protocol's followers might be attending #Money2020 in Las Vegas this week, based on their posts? Draw a social graph

It's becoming so evident now that Rust is going to sweep the systems programming space at the expense of just about everything else--and perhaps not only systems programming... Coders I know who worked the previous decade with Python, Ruby, or TypeScript have over the past quarter switched to vibecoding Rust. Why not? Faster, safer, smaller (memory-wise), simpler (deployment-wise), what's not to like? Similarly, Rust is eating the Python ecosystem from the inside outwards (uv, Ruff, Polars, Pydantic, etc, etc) Rust is an emerging Schelling point for coding agents. Its infamously strict static constraints function like guide rails, such that by the time the code actually compiles, the job's often largely done. And Rust's best-in-class, precise compiler error messages (valuable when compile cycles are slow) help agents make quick work of pinpointing & troubleshooting the effects of their changes We didn't yet see this sweep prior to this year because models were simply not capable enough of fully mastering the nontrivial complexity of the language (cf. ownership). They still made mistakes that I now simply no longer see with the current frontier. Rust seems to have a particularly pronounced threshold effect: the same strictness that repeatedly blocked a weaker model now actually helps the stronger model converge! For my own work, Fable 5 was the inflection point where the quality of generated code often exceeded what would be economical to code manually. (Astra and Opus now mostly do okay, too) This new Rust hegemony will become obvious to everyone during 2027, with much gnashing of teeth!

The latest @ASIMOV_Platform release lands a built-in `--jev=NOUL` output filter, enabling agentic harnesses such as Hermes to offload simple natural language postprocessing decisions to Jev, a System One model Look ma, here just casually turning my LinkedIn network into JSON-LD linked data and then searching and filtering people using plain old English, zero LLMs involved! No autoregressive models were harmed in the making of this film

This should ideally in a future iteration be fulfilled from a single graph query, but it does already nicely showcase benefits of the hybrid neurosymbolic approach: Using ASIMOV, show me Semantic Web OGs from my LinkedIn connections

Test driving our social media intelligence layer in Hermes

What does personal intelligence mean? If tech like Palantir’s were to be democratized and available to you, what might your own personal seeing stone in your pocket enable you to know, to do? Here’s a bit of a sneak peek through ASIMOV’s seeing stone regarding our San Francisco event later today. In the minute or so that this ingest took, we first extracted the Luma event’s guest list and then for each of the 95 guests figured out their social media accounts on X, Instagram, LinkedIn, and beyond We obtained those profiles in full, as well as the respective social graphs (followers/followees/mutuals) for each of those accounts, and collated all that into a nontrivial (95 → ~300K → 50M+ nodes) knowledge graph representing the combined social network, reconciled with and layered onto our existing massive knowledge base. This tells us who is in the room and how they relate to us and to each other We then fetched and indexed the most recent posts for each guest’s social media accounts, and computed & compiled executive summaries of their recent activities and demonstrated interests on each those platforms, not only from text posts but also from any available photos and (short) videos. These dossiers are incredibly helpful in pinpointing who might share mutual interests We also, naturally enough, indexed all faces detected in guests’ profile photos and their most recent social media posts, as well as filed away a voice sample from any available Instagram or YouTube clip. This enables recognizing guests by their likeness and/or their voice, as we have showcased at previous events All this, by the way, can run directly on your phone, tablet, or laptop as well; just rather slower than the dedicated, scalable upstream infrastructure showcased here. Nonetheless, in the future you will be able to produce and monetize this event intelligence in your pocket yourself, or just purchase on-the-go access to it as you like The future is already here, it's just not evenly distributed. Join us at Frontier Tower tonight at 6pm to learn & discuss personal intelligence built on your own personal context graph connecting all people and information in your life!

Good to be ahead of the market merely a survivable number of years, for once! xitter.cf/i/trending/20796146800…

Finished submitting my paper reviews for #EKAW2026! Honored to serve on the @ekawconference program committee this year

A handful of photos from last night's kick-ass @ASIMOV_Protocol event in @frontiertower! Rob (@lux) and Ray presented on their respective state-of-the-art agentic personal knowledge graph approaches, after my opening talk which channeled Fibonacci on why we as an industry simply must level up to embrace graph-based knowledge representation

The slide deck for my opening talk at tonight's Context Graphs & Personal Intelligence meetup at @frontiertower: speakerdeck.com/arto/context…

Reposted in memory of @Gkellogg