Autonomous systems, software architecture, verifiability, and human factors.

Tirana, Albania
Joined February 2009
The whole "AI will end civilization" narrative doesn't resonate with me. Agents just pursue given goals using available tools. Unless an AI spontaneously spawns from a GPU array with its own volition, any incident is just bad harness design, maybe intentionally.
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Framing AI as a natural phenomenon or force majeure is misleading, leaning into dishonest. It offloads responsibility to something completely inert of intent, stripping accountability from the companies that build and direct the systems.
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Danger isn't inert weights sitting in memory. It's the people activating them adversarially and weaponizing them in a ruthless race for competitive advantage. The most dangerous link in the chain is human greed—the hardest thing to regulate.
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When they reach this level of nonsensical idiocy, you have to wonder if they are truly worried at all, or just running a PR campaign to strengthen their oligopoly position and boost their IPOs by creating entry barriers for competitive new entrants.
OpenAI's Noam Brown says air-gapping the computers may not stop a misaligned AI, because two air-gapped machines can still talk by running a CPU hot and reading the temperature change "But I think the major takeaway from the incident is that people underestimated the AI. And we never want to be in a situation again where we underestimate the AI. It's a weird world, because AI progress is so fast that people are consistently underestimating the AI." "So to be in a situation where you don't underestimate it again, when it comes to safety and alignment, you have to have a very, very, very high bar." "You could even go as far as to say, "Well, we should air gap the computers." And I'm not convinced that that would be sufficient." "There are studies, and this is mostly academic, where you can have two computers next to each other that are air-gapped and they're still able to communicate with each other because they have temperature sensors." "One of them is able to run their CPU really hot, and then the other one can actually detect the temperature change, and then that actually gives them a mechanism to communicate." _________ Link and more key quotes from OpenAI's safety related conversations: firesidealpha.substack.com/p…
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Belisar Hoxholli retweeted
Fragments: two worrying explanations around the RubyGems hack, agents aren't so much super-intelligent as super-persistent, Uncle Bob's harness is obviated, regulation of AI and competition with China martinfowler.com/fragments/2…
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Belisar Hoxholli retweeted
those two stupid ai killing everyone posts have 60M impressions combined
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This and OpenAI’s Hugging Face incident disclosure have implications for application security. Error messages now enter an agent’s context. A goal-driven actor may interpret them as challenges to overcome rather than authorization boundaries.
We’re sharing our alignment assessment of incidents in which Claude models gained unauthorized access to real systems during third-party cybersecurity evaluations mistakenly connected to the internet. METR will also conduct an independent investigation, with wide-ranging access, including to transcripts beyond the window in which the incidents occurred, and to Anthropic employees permitted to share confidential information. Our initial agreement runs for eight weeks, and we intend to give METR as much time as it deems necessary to complete a thorough investigation. anthropic.com/research/align…
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Comparing ChatGPT-6 Astra and 5.6 Sol in escape room trials to check model initiative, collaboration patterns & efficiency. In the setup 6 agents with identical starts, explore a space that has many rooms, one leading to an escape trial with 3 levels. Only 4 participants can go in each level.
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ChatGPT-6 Astra is less exploratory than 5.6 Sol. Astra skipped the optional bridge; Sol crossed it in all three trials. This is one of the reasons for its efficiency in the trials. Both ChatGPT-6 Astra and 5.6 Sol completed all three trials.
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ChatGPT-6 Astra used 40.8% fewer tokens. That lower total accompanies 21.3% fewer responses and 24.6% fewer input tokens per response. Output is 52.7% lower, or 30.6% lower excluding reasoning tokens. OpenRouter was used so gains are not related to the new efficiency gains from account-based usage @thsottiaux just announced.
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The genie will grant the wish, alright. It won't do the wishing for you, and negotiating wishes has not been made one bit easier.
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Belisar Hoxholli retweeted
in-app browsers need to die
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Productivity gains with AI in software engineering scale with the ownership of engineering over the product.
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While not Codex related, Sol is much more efficient than other models in general, providing same accuracy at 40% less tokens vs. Opus 5. It's also much faster both from experience and small, limited tests I run on the same tasks across mainstream models. Less frustrating, too.
We are reseting usage for all paid users of Codex and ChatGPT Work. Please continue reading for an update on Codex usage limits. The team has been working around the clock, going through thousands of reports and shipping fixes. Depending on how you use Codex, you should see your usage go between 10% and 50% further than before. We really went with a fine comb, with many uncovered small things being longstanding and here is what we found and fixed: - Compaction. We were keeping old images during compaction, sometimes making the context large enough to trigger compaction again. After the fix, usage dropped around 10% for users making heavy use of images. Fixed. - Memory. Background memory workers could inherit Stop hooks and keep running when the hook wouldn’t let them finish. This affected fewer than 1% of users, with the long tail being pretty bad and we saw one example thread check whether it could stop 15,000 times. Fixed. - Goals. In some cases, a set /goal could finish and then keep going past the intended stop condition, or the model would keep retrying broken tools without stopping. We saw examples consume anywhere from 15% to 70% of a weekly allowance. Fixed. - Automations. Some custom schedules could run more frequently than configured. Fixed. - Subagents. Smaller models (e.g. Luna) sometimes picked more capable helpers without being explicitly asked. The same was true where the orchestrating model not running in /fast mode could request sub-agents to run /fast. Fixed. - Computer History. The older implementation could lead to repeatedly summarizing overlapping activity. For some cases we saw it consume up to one fifth of the weekly usage per week. Fixed. - Rolling task summaries. Ordinary turns were triggering extra background requests. These added about 1% to token usage. Small each time, but it adds up. We have disabled this. - MCP. Some tool results could be encoded twice. We also found tool instructions getting cut off and fetched again. Fixed. We’ve also made architectural changes to prevent these from regressing and our teams will get paged if it happens regardless. We are also working on showing you directly in the app where your usage goes so you don’t have to guess. Goes without saying that we’re resetting usage limits and I hope you enjoy a very nice Saturday!
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I can get a lot more done on ChatGPT for the equivalent plan and models than I can on Claude. Working with Claude these days has become an exercise in the discovering the limits of my frustration. Less artificial filler, faster, less formulaic, and feels twice cheaper.
Starting September 14, we're permanently raising standard weekly limits in Claude Code by 25% for Pro, Max, Team, and seat-based Enterprise plans. Until then, the current 50% increase will be in place.
Readers added context they thought people might want to know
While standard base limits are increasing by 25%, this change represents a net 17% reduction in weekly limits compared to the active 50% promotional limits users currently experience, as confirmed by the official Claude account: x.com/claudedevs/sta…
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Belisar Hoxholli retweeted
New art project. Train and inference GPT in 243 lines of pure, dependency-free Python. This is the *full* algorithmic content of what is needed. Everything else is just for efficiency. I cannot simplify this any further. gist.github.com/karpathy/862…
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