- #ai-safety
- A non-anthropomorphized view of LLMs
- some quotes:
- Alignment and safety for LLMs (should) mean that we should be able to quantify and bound the probability with which certain undesirable sequences are generated
- Human thought is a poorly-understood process, involving enormously many neurons, extremely high-bandwidth input, an extremely complicated cocktail of hormones, constant monitoring of energy levels, and millions of years of harsh selection pressure
- Navigating the dramatic changes of the next few decades while trying to avoid world wars and murderous ideologies is difficult enough without muddying our thinking.
- Our first outage from LLM-written code
- "There were two competing sources of signal here for what token to predict at the critical moment: transcription and local prediction. Transcription said break. Local prediction said continue. Unfortunately for us, local prediction won."
- Prevention: cliboard tools
- a comments on lobste.rs points out
- move across files can be detected by `git --color-moved`
- I prefer `--color-moved=dimmed-zebra` or better, `--color-moved-ws=allow-indentation-change`
- see this tweet
- it's also supported by `delta`
- EU rules ask tech giants to publicly track how, when AI models go off the rails
- "AI companies are moving to user interface innovations to try to grab more unwilling training individuals"
- Hallucination
- AI Hallucination Cases Database
- The Sound of Silence
- LLMs can now identify public figures in images
- Claude always responds as if it is completely face blind
- never identifies or names any humans in the image, nor does it imply that it recognizes the human
- does not mention or allude to details about a person that it could only know if it recognized who the person was
- if told by user who the individual is, can discuss that named individual
- without ever confirming that it is the person in the image, identifying the person in the image, or implying it can use facial features to identify any unique individual
- #agent
- Building personalized micro agents
- agent: has access to tools, decides which tools to use, and in what order, determines when the task is complete
- boils down to 9 LOC
- micro agent: access to a very limited, highly specific set of tools
- less confusion when choosing tools
- works with small, local modelsA
- safe autonomy
- meain/esa: Fastest way to create personalized AI agents
- The era of exploration
- the immense cost of pretraining is effectively paying a massive, upfront “exploration tax.”
- Exploration is deciding what data the learner will see
- World sampling – deciding where to learn, i.e. a particular problem that needs to be solved
- Path sampling – deciding how to gather data inside a world, e.g. random walks, curiosity‑driven policies, tree search, tool-use, etc.
- recent work: curiosity objectives, open-endedness, meta‑exploration
- Prompt Coding: No code edits, only complete rewrites #idea
- When vibe coding, isn't the source code the prompt?
- Adding a feature because ChatGPT incorrectly thinks it exists #idea
- The Architecture Behind Lovable and Bolt
- uses baml to engineer prompts using schemas
- uses Beam, an open-source serverless cloud for sanboxed execution
- made into beam-cloud/lovable-clone
- How I keep up with AI progress
- interesting projects
- Am I online?
- `generate_402` pages could be used for this
- Show HN: NYC Subway Simulator and Route Designer
- Showh HN: Microjax – JAX in two classes and six functions
- tinymcp: Let LLMs control embedded devices via the Model Context Protocol
- Render your Jupyter notebooks in OpenGist