Anyone else noticed that the amount of tokens produced is much larger? I mean, we're not using Anthropic's API, but I've noticed it in Perplexity, where the model just goes on and on and on. The moment I ask it to summarize, it condenses everything into two short bullets.
Just wanted to share the prompt I use when correcting grammar and writing with an LLM. Paste this into ChatGPT, Gemini, etc., followed by the passage of text.
>Rephrase the following text to improve clarity and correct any grammar or punctuation errors, while keeping the original meaning intact, do not add any external information or change the meaning, hedging, etc. maintain the level of abstraction, do not generalize, or provide concrete examples that doesn't appear already in the text below. If XXX appears, substitute it with a correct word or phrase, based on the context. Do not add interpretations, implications, or inferred meanings. Do not rephrase if not necessary, only rephrase if it improves clarity or corrects grammar or punctuation errors. If you'd like to offer additional suggestions, such as improvements to logic, additional information, or alternative interpretations, please do so in a postfix section in the end of the rephrased text you provide that keep the meaning intact, the added section should clearly be labeled as "Suggestions" or "Additional Information". The suggestions should be concise and relevant to the content of the text, presented as a list of numbered bullet points we can further discuss. Do not use em-dashes, clauses and semicolons, use comma and period instead. Aim to avoid using colons.
This lets you write much faster, think of what LaTeX did for formatting. You simply skip past any grammar mistake you know you've made. The revised text includes the fix, so you also get to learn from it.
Another tip, use medium thinking effort, which in my experience tend to produce better results. Higher thinking effort produce some over-edits.
We build a hosted research agent, and the Stop button turned out to be something worth writing about :)
When the agent runs in a server, your browser only shows a live view of the work. Closing the tab or losing wifi shouldn't stop the agent, so orderly cancellation is needed.
When you are developing a product that perform token accounting, i.e, that tracks the usage of each operation and attribute it to the user, cancelling a running agent should finalize correctly, otherwise tokens can be misattributed, or attributed after the fact. So the UI should transition between working -> cancelling and cancelled + report for the tokens that were consumed.
That's a short version, below you can find the full technical write up of what this means.
Full write up: https://agentbayes.com/blog/stopping-a-streaming-llm-agent
Some of what's in there: prompt injection and tool-use attacks, agent-in-the-middle attacks on inter-agent messages, the TRiSM framework, scalable oversight, and current benchmark results. One stat that stuck with me: none of sixteen mainstream agents scores above 60% on Agent-SafetyBench, and average attack success rates for prompt injection, memory poisoning and tool poisoning are above 80%.
Wanted to show some appreciation to the people working on Nuxt. It has been very useful for us, so here's a quick rundown of how we use it.
The site is the usual SaaS mix: marketing pages, blog, docs, pricing, plus public share pages where users can publish an interactive mindmap at /m/:slug. Our first instinct was static site + separate viewer app, but route rules let us keep it all in one Nuxt app:
Everything content-shaped (homepage, blog, docs, legal) gets prerendered through Nitro. Pricing is SWR with a 5 minute TTL and reads the plan catalog from our backend through a Nitro server route, so I never have to update pricing copy in the website repo when plans change. The share pages use the same SWR boundary: the server renders OG tags, canonical URL and a plain HTML outline of the mindmap for crawlers, then a <ClientOnly> canvas takes over in the browser.
One issue we hit, and the workaround: crawlLinks: true does not discover pages behind a redirect. /docs is a redirect-only route, and the crawler wouldn't follow it into the nested docs pages, so we ended up listing all ~30 docs routes explicitly in nitro.prerender.routes. If anyone has a cleaner alternative, let me know.
Rest of the stack -> Nuxt Content with typed collections for blog and docs, sitemap with zeroRuntime so the sitemap is a build artifact, Shiki dual-theme highlighting, Tailwind via PostCSS.
Site: https://agentbayes.com
Sample public share: https://agentbayes.com/m/jQS6rZ
Hey, sharing a mindmap I made on AI agent safety and alignment, backed by citations with full provenance. I’m disclosing that I’m also currently working on Agent Bayes, the tool used to build the mindmap. I think this subject is still underdeveloped compared with the pace of AI progress. I’d be happy to get your feedback on the resulting mindmap, and to learn if it helps anyone.