The more autistic traits you have, the more intense your “Intolerance of Uncertainty” becomes. Ambiguity triggers your brain to flip into immediate overdrive. To put that into perspective, when faced with a partner slamming a cabinet door and dropping a cold “I'm fine” during an argument, or a boss sending a vague “Let's chat tomorrow” email with zero context, you immediately begin to pick apart the threat.
Your brain does not try to process the emotion first. Higher autistic traits have a harder baseline reading emotions (the -0.44 link), their absolute hatred of unpredictability (+0.67) forces them to deploy logic.
Once you systematically map out the situation, you then use affect labeling to pin a word to what you are experiencing. This is what finally drops your stress levels and restores total control.
*Data based on a sample of 505 adults (aged 20–39) evaluated across the general population using the 50-item Autism-Spectrum Quotient.
we are still so early on e-ink
i do most of by programming on e-ink now. 60hz 1440p @ 60hz, works great (though light mode is a must)
can’t wait for the next few years of upgrades
If leading AI companies are indeed approaching the point of recursive self-improvement, a coordinated, verifiable, and universally applied pause is probably the only responsible solution to mitigate several major AI risks; at least until safety guarantees are developed and demonstrated. Ensuring that such a moratorium is respected would require sincere collaboration between various countries and companies, but I definitely believe it is achievable if others follow in @AnthropicAI's footsteps.
As an AI Engineer. Please learn
>Harness engineering, not just prompt engineering
>Context engineering, not just long prompts
>Prompt caching vs. semantic caching tradeoffs
>KV cache management, eviction, reuse, and memory pressure at scale
>Prefill vs. decode latency and why they optimize differently
>Continuous batching, paged attention, and throughput optimization
>Speculative decoding vs. quantization vs. distillation tradeoffs
>INT8, INT4, FP8, AWQ, GPTQ, and when quantization hurts quality
>Structured output failures, schema validation, repair loops, and fallback chains
>Function calling reliability, tool contracts, argument validation, and idempotency
>Agent guardrails, loop budgets, tool budgets, and termination conditions
>Model routing, graceful fallback logic, and degraded-mode UX
>RAG architecture: chunking, embeddings, hybrid search, reranking, and freshness
>Retrieval evals: recall, precision, grounding, attribution, and citation quality
>Evals: golden sets, regression tests, adversarial tests, LLM-as-judge, and human evals
>LLM observability as a first-class discipline: traces, spans, tokens, latency, errors, and drift
>Cost attribution per feature, workflow, tenant, and user journey not just per model
>Safety engineering: prompt injection defense, data leakage prevention, and permission boundaries
>Multi-tenant isolation, cache safety, and cross-user context contamination prevention
>Fine-tuning vs. in-context learning vs. RAG vs. distillation and when each is the wrong tool
>Latency, quality, cost, and reliability tradeoffs across the full inference stack
>Production failure modes: hallucinated tool calls, malformed JSON, stale retrieval, runaway agents, and silent eval regressions
Two years ago my son got a job offer from Anthropic
$320k base salary, $200k in stock options at the time
While my family was celebrating, I pulled my son aside
“Son, you need to decline this offer. Those stock options aren't real, it's fake money American companies print so they don't have to pay real salaries”
“But dad, this can be one of the biggest companies in the world, I can be worth millions” he argued with me
I burst out laughing
“Biggest in the world? It's an overhyped AI company that nobody knows in Europe” I said
He went silent
“Siemens, Allianz, Lidl. Those are companies you need to work at, if you want to build a real career” I added
Two years later, my son works at Siemens in Munich making €58,000 a year, real cash
I don't know what happened with that Anthropic business, but in Germany nobody has heard of them yet
It will probably be bankrupt soon like most American startups
I'm glad I saved my son from the biggest career mistake of his life
My friend recently made €3,000 investing in the stock market
Incredible
I asked how he made such a large sum in only 2 years
He put €150k into a portfolio of large EU tech companies
Just 24 months later, his stake had appreciated by well over €3k
Unheard of returns in European teach
I urged him to remit unrealized capital gains tax to the government immediately
Even though that tax hasn't been passed yet, it's always good to get ahead of things and reinvest some of your profits into the ecosystem that made your wealth possible
He sent €1,750 to the government and plans to use the rest of the gains to pay for his next 45 years of healthcare expenses (once he sells)
Live reactions to this:
- defines catastrophic risk as death/serious injury to >50ppl or more than $1b in damage; calls out CBRN and loss of control risks
- critical safety incident defined as modification or exfiltration of weights, failure of risk mitigation measures, or loss of control
- frontier model defined as trained on >10^26 FLOPs
- frontier developer defined as trained a frontier model AND has >$50m in revenue
There are few things as heartbreaking for me as Ted Chiang and Greg Egan, two of my favorite sci-fi writers, being so utterly incurious about the second generally intelligent system we've ever discovered in the known universe.
I think it is really worth reading this piece on RSI at Anthropic.
There is a bit of navel-gazing, some marketing, and a lot of very sincere beliefs about what Anthropic thinks is likely in the near future of AI that you probably want to be aware of. https://t.co/A5yxryBjHv
everyone is building an agent or a tool
you don't want an agent or a tool, you want a reactor
I've been working on something cool and I think you'll like it
it's simple: an agent session DAG that keeps a declared world-model up to date in an efficient (memoized) render
each render node is an agent session: you declare the desired state with OpenProse markdown files
once invoked, each agent session acts as the provider. the agent session uses the open source openai-agents-sdk, extensible however you like with any model (I use with opus, sonnet, haiku)
the facets of the world-state are memoized, so not every agent has to run on every event, saving you on inference
if that sounds a lot like React or dataflow, that's because even in our brave new world the wisdom of the agents holds fast
@pkmital@aionthelot I think you would've liked what @eshear had to say at @frontiertower tonight about alignment capacity's path dependence on cooperative agent-human environments.
SITUATION DETECTED: Senator Bernie Sanders says he'll meet with Sam Altman tomorrow to discuss a datacenter moratorium, international cooperation to avoid superintelligence, and compensation for training data. Altman requested the meeting.
it’s been quite sad to me that my favourite science fiction author (Ted Chiang) has not really offered a thought-provoking or new perspective on the most consequential technology of our time