2026-10-11

Real time communication isn't one size fits all. A common mistake in system design is picking WebSockets when all you really needed was server-to-client streaming. I wrote a short 3 minute architectural comparison of the trade offs here: What do you usually reach for first when building real time features ?

At this point there is strong evidence that AI does not increase velocity much outside of startups because it moves the bottleneck to review, and does not solve the political, procedural or practical friction in large enterprises that soaks up most time anyway. Various academic studies have also shown low impact to overall productivity at the firm and individual level. But it seems like this reality is not getting through to engineering leaders AT ALL. My questions is, under what circumstances w

Microsoft’s decision model for agents and workflows Discussion | Link

An 11MB local model for typed decisions in one pass Discussion | Link

GPU-parallel simulation provides abundant robot interaction, but existing benchmarks rarely combine this scale with heterogeneous manipulation tasks and standardized multi-task RL evaluation. We introduce Hebero (Heterogeneous Benchmark for Robot Learning), a GPU-parallel Isaac Lab benchmark that enables efficient joint training and evaluation of a single policy across all 40 heterogeneous tasks. Scaling experiments show that increasing parallel replicas per task improves success under a fixed w

Existing spatial reasoning benchmarks mainly test spatial perception: reading off relations already visible in the input. Yet real-world spatial intelligence demands predictive spatial reasoning: constructing a scene from observations, anticipating how an intervention changes it, and reasoning about the unseen outcome. We introduce SpaceCast-Bench, the first benchmark to directly and diagnostically evaluate this capability. Built around an observe-transform-infer framework, its 3,862 questions f

Optimizing deployed AI systems increasingly amounts to editing prompts, skills, harnesses, and code rather than model weights. Existing approaches commonly optimize these artifacts through propose-evaluate-select procedures, where candidate configurations are evaluated and only those meeting an acceptance criterion are selected. Yet our analysis shows that discarded candidates often contain information critical for subsequent optimization. Discarding them causes later proposals to revisit the sa

I am a data scientist who started working in the industry before the LLM revolution. Back then, Jupyter Notebooks were a perfect fit for the classical DS pipeline: EDA -> data prep -> fit -> eval -> tune -> save model artefact and notebook. Lately, I have been thinking a lot about how agentic development and LLMs are changing the way data scientists work. Especially in classical ML applications, where you still need to explore data, run experiments, check different hypotheses and decide what to

I remember hearing a while ago that Dwarf Fortress didn't use version control. That fact has been lodged in my head, because given its inherent complexity I have trouble imagining a codebase that would benefit from version control more . That's art. I went looking and I'm sad to report that the days without version control appear to have ended. Quoting Tarn Adams over time: March 2013: "I don't use version control -- I didn't like the feeling of having the code get committed into a black box thi

Recent 3D world models generate photorealistic, explorable scenes that remain frozen in time. OuroWorld is a mask-free framework that turns any static 3D Gaussian Splatting scene into a 3D cinemagraph: a dynamic scene with vivid, diverse motion looping seamlessly from any viewpoint. A vision-language model infers plausible dynamics and guides a video model to synthesize a reference video, which we lift and complete into multi-view videos. To learn from this imperfect supervision, we propose Inco

arXiv:2610.10954v1 Announce Type: new Abstract: Heuristic search for a plan can store exponentially many states, even when its heuristic is almost perfect. We instead learn search control, one specification per domain, written as an indexical policy: a generalized policy with registers that hold objects and modes that sequence its rules. We add the choose rule, which loads an object into a register and marks a backtracking point, where one candidate suffices; every other rule must work for all o

arXiv:2610.10906v1 Announce Type: new Abstract: Human communities are governed by normative systems: shared standards that produce \textit{norms} dictating acceptable behavior, enforced through community sanctioning. Aligning increasingly autonomous AI systems with these norms is a central alignment challenge, complicated by the fact that norms are vast in number, change quickly, and are often arbitrary (e.g., dress or language conventions). Thus, alignment requires \textit{normative competence}

Original study Link here Another study Link here and excerpt below: AI helped participants better discern what’s real – and resulted in a 21% higher chance they would make the right call. But their unassisted performance, when reviewing new images without AI’s help, grew 15.3% worse in the experiment’s fourth week. “These results indicate that while AI may help immediately, it may ultimately degrade long-term misinformation detection abilities,” the study noted. Not just chatgpt but AI coding is

The search engine built for scientific AI agents Discussion | Link

Nvidia reportedly halts GeForce RTX 5090 production in favor of AI data center and professional GPUs — impending supply drought expected to drive up prices, RTX 5080 24GB rumored as new gaming flagship

Review your code and your agents' changes before you ship Discussion | Link