Every week, I organize 400 to 700 links into roughly 60 categories as part of my ongoing effort to learn about AI. This is my personal notebook, which I enjoy sharing with friends… a hobby and a labor of love, rather than a commercial publication or product.
If you arrived here through a search or shared link, this page collects the links I found for AGI for the week ending August 28, 2026.
As part of my learning process, I like to automate the category covers. It gives me a chance to learn Python and APIs.
This week’s cover prompt was written using Claude Opus 4.7, and the image was generated using Nano Banana 2.1.
Category Cover Image Prompt: A 16:9 mid-1980s murder-mystery comedy movie poster of a wide gothic Victorian mansion in stark black-and-white pen-and-ink engraving with dense crosshatching on pure black, with warm amber windows showing costumed suspects claiming to be the prophesied heir of general intelligence: a robed oracle with a benchmark scroll, a lab-coated scientist with a scaling-law chart, a child prodigy at a chalkboard of equations, plus still-life windows of crystal balls, stacked IQ tests, an empty velvet throne, and one window with a giant fogged question mark on the glass. At the center open front door stands the same slim upright silver-haired butler in his sixties with pencil mustache, raised eyebrow, black tailcoat, white gloves, holding a silver tray with a sealed envelope marked by a tiny wax infinity symbol, with the fat rounded white title AGI set above the roofline like a movie title.
This Week in AGI News
Summary by Claude Sonnet 5.5 based on this week’s links:
- ARC-AGI-3 claim: NVIDIA said its general-purpose coding agent, AVO, completed all 183 levels across the 25 public environments, with no instructions, rules, or stated goals. Chollet called it very nice work and noted it uses deep-learning-guided synthesis of symbolic world models, meaning it writes programs to capture what it has learned.
- Recursive self-improvement under scrutiny: Much of the week was about whether agents can improve how AI is trained. A Tsinghua study of 1,338 real post-training runs reportedly found agents can train, debug, and evaluate models but hit a revealing limit. Another paper argues self-editing systems must leave part of their editing machinery untouched, capping meta-depth at roughly two.
- Money and compute: Dwarkesh Patel and Dylan Patel discussed lab economics, including a shift from inference to training as self-improvement nears. Patel also asked whether AI investment could trigger a sovereign debt crisis, noting a data center's capex can reportedly be repaid by about a year of leasing.
This summary was generated by Claude Sonnet 5.5 to help you explore the links below. Rest assured, I select, organize, and check the links by hand in Google Sheets, and write the introduction and personal commentary in The Main Newsletters myself each week as a labor of love.
This week's links related to AGI
Our general-purpose coding agent just scored 100% on the ARC-AGI-3 interactive reasoning benchmark. NVIDIA AVO completed all 183 levels across all 25 public environments, figuring out what to do with no instructions, explicit rules, or stated goals.”
https://x.com/NVIDIAAI/status/2090786258981466231
Had a lot of fun chatting again with my twin brother @dylan522p We went through lab economics over the next few years – the shift from inference to training as RSI draws near; and how Anthropic and OpenAI are on track to control most of the world’s usable FLOPs within the next”
https://x.com/dwarkesh_sp/status/2092280377255551320
OpenAI decided to keep the chip team small and use AI for a an improvement loop Early stages of Hardware in the loop RSI?”
https://x.com/beffjezos/status/2092418951586390137
Solving recursive self-improvement with a harness. The big question with the agent harnesses I use is: how does it support RSI and compound on every iteration? What capabilities do I need to own the harness? What are the best solutions right now? Something important to”
https://x.com/omarsar0/status/2091915906305704015
1/ Recursive self-improvement (RSI) depends on agents improving how AI systems are trained … not just tuning hyperparameters, but improving the training algorithm itself. We tested this directly with AI4AI-Bench: 10 real research repositories spanning 10 distinct algorithm”
https://x.com/EinsiaAI/status/2090854778301771909
This paper is very worth reading if you’re following AI agents and the road to Recursive Self-Improvement (RSI) @Tsinghua_Uni looked at 1,338 actual AI post-training runs and found a pretty revealing limit. Agents can do the work. Train models, debug failures, evaluate results,”
https://x.com/TheTuringPost/status/2092605320703168706
Interesting new approach to recursive self-improvement in agents. Systems that add a meta-level hold that level fixed. Systems that edit themselves have to leave part of their own editing machinery untouched to stay stable, which caps realized meta-depth at roughly two. Meta^n”
https://x.com/dair_ai/status/2092698602401599868
Aloha! 🌺Introducing Ornith-1.5, a family of open-source LLMs spanning 9B Dense, 35B MoE, and 397B MoE, trained with self-improving strategies. It achieves state-of-the-art performance among open-source models of comparable size and delivers performance comparable to Claude Opus”
https://x.com/ornith_/status/2090074077084127302
This is very nice work from NVIDIA. Like all high-performing approaches on ARC-AGI-3, it uses deep learning-guided on-the-fly synthesis of symbolic world models, i.e. navigating the world by generating programs to represent what you know. To be clear, like with several other”
https://x.com/fchollet/status/2090838046937645398
as expected, GLM 5.3 Flash is pretty bad at vision interesting how different models end up specializing in completely different things some are great at coding, others at computer vision no AGI yet, I guess 1/n aerial and satellite images, crops link:
https://x.com/skalskip92/status/2092748209802154201
Could AI investment start a global sovereign debt crisis? If you look at the SpaceX-Google deal, it is already the case that the entire capex for the data center can be paid off by leasing it out for roughly 1 year. As we get closer to AGI, the returns on compute will be”
https://x.com/dwarkesh_sp/status/2092387983345516960
Many important and bold ideas in @BillGates’s new essay on the impact of AI need to be discussed… Token Tax and a protected ‘nature reserve’ for certain jobs, are just two of them.”
https://x.com/mustafasuleyman/status/2092559789016395834
Math is fine, but vague posting from labs about new models should only be allowed if the post consists solely of the output from the prompt in the Sparks of AGI paper: “Draw a unicorn in TiKZ.
https://x.com/emollick/status/2091995606516441405
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