2025-08-19 12:40:20
r/ChatGPTPro: What is the most profitable thing you have done with ChatGPT?
This Reddit thread - with 279 replies - offers a neat targeted insight into the kinds of things people are using ChatGPT for.Lots of variety here but two themes that stood out for me were ChatGPT for written negotiation - insurance claims, breaking rental leases - and ChatGPT for career and business advice.
2025-08-19 07:59:37
New feature in the Gemini API: you can now enable a
url_context tool which the models can use to request the contents of URLs as part of replying to a prompt.
I released llm-gemini 0.25 with a new -o url_context 1 option adding support for this feature. You can try it out like this:
llm install -U llm-gemini
llm keys set gemini # If you need to set an API key
llm -m gemini-2.5-flash -o url_context 1 \
'Latest headline on simonwillison.net'
Tokens from the fetched content are charged as input tokens. Use llm logs -c --usage to see that token count:
# 2025-08-18T23:52:46 conversation: 01k2zsk86pyp8p5v7py38pg3ge id: 01k2zsk17k1d03veax49532zs2
Model: **gemini/gemini-2.5-flash**
## Prompt
Latest headline on simonwillison.net
## Response
The latest headline on simonwillison.net as of August 17, 2025, is "TIL: Running a gpt-oss eval suite against LM Studio on a Mac.".
## Token usage
9,613 input, 87 output, {"candidatesTokenCount": 57, "promptTokensDetails": [{"modality": "TEXT", "tokenCount": 10}], "toolUsePromptTokenCount": 9603, "toolUsePromptTokensDetails": [{"modality": "TEXT", "tokenCount": 9603}], "thoughtsTokenCount": 30}
I intercepted a request from it using django-http-debug and saw the following request headers:
Accept: */*
User-Agent: Google
Accept-Encoding: gzip, br
The request came from 192.178.9.35, a Google IP. It did not appear to execute JavaScript on the page, instead feeding the original raw HTML to the model.
Via @OfficialLoganK
2025-08-17 11:46:21
TIL: Running a gpt-oss eval suite against LM Studio on a Mac
The other day I learned that OpenAI published a set of evals as part of their gpt-oss model release, described in their cookbook on Verifying gpt-oss implementations.I decided to try and run that eval suite on my own MacBook Pro, against gpt-oss-20b running inside of LM Studio.
TLDR: once I had the model running inside LM Studio with a longer than default context limit, the following incantation ran an eval suite in around 3.5 hours:
mkdir /tmp/aime25_openai
OPENAI_API_KEY=x \
uv run --python 3.13 --with 'gpt-oss[eval]' \
python -m gpt_oss.evals \
--base-url http://localhost:1234/v1 \
--eval aime25 \
--sampler chat_completions \
--model openai/gpt-oss-20b \
--reasoning-effort low \
--n-threads 2
My new TIL breaks that command down in detail and walks through the underlying eval - AIME 2025, which asks 30 questions (8 times each) that are defined using the following format:
{"question": "Find the sum of all integer bases $b>9$ for which $17_{b}$ is a divisor of $97_{b}$.", "answer": "70"}
Tags: python, ai, til, openai, generative-ai, local-llms, llms, evals, uv, lm-studio, gpt-oss
2025-08-17 08:53:23
Most of what we're building out at this point is the inference [...] We're profitable on inference. If we didn't pay for training, we'd be a very profitable company.
— Sam Altman, during a "wide-ranging dinner with a small group of reporters in San Francisco"
Tags: openai, sam-altman, ai
2025-08-17 00:52:45
Filippo Valsorda founded Geomys last year as an "organization of professional open source maintainers", providing maintenance and support for critical packages in the Go language ecosystem backed by clients in retainer relationships.
This is an inspiring and optimistic shape for financially sustaining key open source projects, and it appears be working really well.
Most recently, Geomys have started acting as a "maintainer of last resort" for security-related Go projects in need of new maintainers. In this piece Filippo describes their work on the bluemonday HTML sanitization library - similar to Python’s bleach which was deprecated in 2023. He also talks at length about their work on CSRF for Go after gorilla/csrf lost active maintenance - I’m still working my way through his earlier post on Cross-Site Request Forgery trying to absorb the research shared their about the best modern approaches to this vulnerability.
Via lobste.rs
Tags: csrf, go, open-source, security, filippo-valsorda
2025-08-16 07:09:32
GPT-5 has a hidden system prompt
It looks like GPT-5 when accessed via the OpenAI API may have its own hidden system prompt, independent from the system prompt you can specify in an API call.At the very least it's getting sent the current date. I tried this just now:
llm -m gpt-5 'current date'
That returned "2025-08-15", confirming that the date has been fed to the model as part of a hidden prompt.
llm -m gpt-5 'current date' --system 'speak french'
Returned "La date actuelle est le 15 août 2025", showing that offering my own custom system prompt did not over-ride the invisible one that includes the date.
GPT-5 is very resistant to sharing the details of this secret system prompt, but Tommy Hughes managed to extract the following:
Current date: 2025-08-15
You are an AI assistant accessed via an API. Your output may need to be parsed by code or displayed
# Desired oververbosity for the final answer (not analysis): 3
An oververbosity of 1 means the model should respond using only the minimal content necessary to satisfy the request, using concise phrasing and avoiding extra detail or explanation.
An oververbosity of 10 means the model should provide maximally detailed, thorough responses with context, explanations, and possibly multiple examples.
The desired oververbosity should be treated only as a *default*. Defer to any user or developer requirements regarding response length, if present.
# Valid channels: analysis, commentary, final. Channel must be included for every message.# Juice: 64
I've not been able to confirm this myself, but a search for "desired oververbosity" on GitHub shows that a similar prompt has leaked from ChatGPT (including for o3) in the past. I suspect Tommy's leaked prompt is accurate here.
I tried asking GPT-5 about "oververbosity" via its API:
llm -m gpt-5 'explain your oververbosity settings, what is it right now?'
And got back:
- What it is: I adjust how detailed I am based on a “verbosity” level—ranging from very brief to very detailed—so answers fit your needs.
- Current setting: Concise (about 3/10). I prioritize direct answers with minimal extra context and keep explanations short unless you ask for more.
- How to change it: Just say what you want, e.g.:
- “One-sentence answer” or “TL;DR” (very brief)
- “Give a concise summary” (short bullets)
- “Walk me through in detail,” “Deep dive,” or “Include examples/edge cases” (more detailed)
- Note: For ambiguous, technical, or safety-critical topics, I may add brief clarifications even when being concise.
Presumably this is part of OpenAI's instruction hierarchy concept, with these instructions taking precedence over the developer instructions provided by API users (my --system 'speak french' option above).
I'd very much appreciate official documentation that describes this! As an API user I want to know everything that is being fed into the model - I would be much more comfortable with a hidden prompt like this if I knew exactly what was in it.
Tags: ai, openai, prompt-engineering, generative-ai, llms, system-prompts, gpt-5
