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10

"The Making of the Atomic Bomb" by Richard Rhodes is a particularly engaging read. Rhodes manages to record history as a character-driven story without sacrificing detailed elements of physical chemistry and - at times - philosophy. His treatment of Szilard and Bohr is outstanding. I also recently finished "A Cage Went in Search of a Bird", which is an anthology of Kafka-esque short stories. I'd recommend it if only for reading "God's Doorbell" (which imagines swarm-behavior RSI building the Tower of Babel).

faixon · 32 minutes ago
12

Credentials seem to be another big part of this lock-in problem. If an always-on agent has permanent access to all your internal tools, moving agents means moving a huge pile of secrets and permissions too including api integrations you forgot even existed. And as a secondary consequence, imagine you've connected your whole life to your agent and used it for 5 years, and then your agent gets prompt injected. Everything about you/your life is stolen and it's just straight up over

layerv-ai · 1 hour ago
27

The distinction you’ve drawn (the decision is made by the deterministic engine; the LLM merely explains it) matches something I’ve seen in another field. I was testing the agent’s memory: the schema of a vehicle changes mid-run, and the agent must make a call appropriate to the new schema at the end. Results: - A 20-line ‘the latest definition wins’ rule: 40/40 - Providing the entire history to the model: 38/40 - Retrieval-based memory: Mem0 (open-source) 3/15, TF-IDF + recency 1/40 When presented with the correct information, the model used it without issue. What it couldn’t reliably do was decide which information was still valid. I’m curious about versioning on your end. When a rule changes, what happens to decisions made under the old rule? If someone asks six months later, “Why was this application rejected?”, does the explanation refer to the rule version at the time of the decision, or to the current one? Does the RAG side know that a policy passage has become invalid, or could it retrieve the old document to explain a new decision?

keparlak · 1 hour ago
29

I ran a statistical analysis of my Claude Code API request data. In the linked blog post, I show that prompt caching leads to input token cost savings of around 85 percent, even though I'm not running fully autonomous sessions often. I show how cost relates to the cache miss rate, and I model the request dynamics with two timescales, one corresponding to fast processes such as agentic tool calls, and the other to slow processes such as reviewing the LLM output. These two timescales also appear in the data. The cache misses come primarily from the slow timescale. Running these measurements on your data can help you determine how cost-efficient your usage pattern is and what drives your cost. You can use this to adjust your work pattern (or configure the system you're building) by, for example, setting a non-default cache expiry time, or consciously trimming the mean time of your slow timescale.

luka_a · 2 hours ago