2026-09-06

I've been trying the latest models from the frontier labs and honestly, after extensive testing I can not tell the difference between the best open source options. I think the differences are now marginal but the labs are doing heavy marketing to convince the public into paying more for tokens as they prepare to go public. Can't help but see the similarities between the dot com bubble and AI in terms of a very insular environment where the technology will survive but the business models may not.

For scientific progress, we need benchmarks that test the limits of state-of-the-art models, and evaluation methods that inform us about failure cases. As models get stronger, standard benchmarks for machine translation are approaching saturation. Further, automatic translation metrics are unreliable, vulnerable to reward-hacking, and provide unactionable assessments. Even gold human evaluation is not problem-free, because it often lacks reproducibility, objectivity, and scalability. Overall, th

Adaption Labs has released Invent a Dataset, which generates a structured, training-ready dataset from a description of the behavior you want a model to learn. There is no seed corpus, no schema design, and no labeling guide. A single datasets.invent call sets domains, row count, output format, and language expansion, and the rows download as JSONL, JSON, CSV, or Parquet. The dataset ID then passes straight into AutoScientist, closing an intent-to-trained-model loop. The post Adaption Labs Intro

I recently set up a 3-node Kubernetes homelab on bare metal (on Dell OptiPlex) using Talos Linux, mainly to have a proper environment for learning, testing failures, networking, storage, GitOps, observability, and general Kubernetes experiments. The setup uses three control-plane/etcd nodes with a shared Kubernetes API VIP, and I documented the full process from network planning and DHCP reservations to Talos installation, per-node configs, bootstrap, workload scheduling, and HA testing. I’ve wr

Full credits to @artificialisabel from X!

The Seattle Times and Newsday accused the two AI giants of using their content without permission.

Ling 3.0 Tiny still seems to be leading the pack despite only having 1.3B active

如此“反骨”的方法，具体又是怎么实现的？

Make new code the last resort Discussion | Link

Ever since I have been tinkering with Proxmox, I noticed the huge amount of support it seems to get on Reddit subs and generally in the "homelab" cohort. But when you mention this to any sysadmin, they look at you with a smirk like you had just told them about your Lego side project or even outright ask what it is with serious face, no joking. And these are not all just "VMware dinosaurs", they are versatile folks from providers which could easily add something like Proxmox to their stack. I won

Two more news organizations are suing OpenAI and Microsoft over the supposed use of their journalism to train AI.

SpaceXAI’s Grok Bot has the same level of programming power as OpenClaw, but it’s programmable at a different level of abstraction.

While philosophers ponder AI consciousness, the models have ideas of their own.

arXiv:2609.03478v1 Announce Type: new Abstract: We report on our ongoing project to develop a computational pipeline, AutoGraphForge, for an automated graph-theoretic conjecturing-refuting-formalizing-proving system. Conjecture generation is counterexample-guided and runs in rounds: a Graffiti3 generator proposes conjectures over a small, evolving snapshot table $T$ (initially a few hundred graphs with their computed invariants) that grows only by counterexamples to its own conjectures. A novelt

Knowledge-Based Visual Question Answering (KB-VQA) relies on retrieving external information to answer queries involving long-tail entities. However, existing retrieval pipelines predominantly employ CLIP-style dual encoders, which prioritize surface-level visual similarity over entity-level semantic alignment. This paradigm often fails when semantically identical concepts exhibit large visual variations or when distinct entities appear visually similar. To address this, we propose KBMR, the fir

Camera-conditioned world models generate interactive videos in which commanded actions should induce the expected scene changes while appearance, geometry, and temporal dynamics remain coherent. Existing rewards assess these requirements separately: geometry-based rewards estimate trajectory execution but cannot judge the visual quality of the executed motion, whereas image-based rewards measure frame quality without capturing action execution or temporal dynamics. We posit that a vision-languag

AI直接吐出正确答案，但最关键的可能不是答案