Simon Willison’s Weblog

Rich Pixels. Neat Python library by Darren Burns adding pixel image support to the Rich terminal library, using tricks to render an image using full or half-height colored blocks.

Here's the key trick - it renders Unicode ▄ (U+2584, "lower half block") characters after setting a foreground and background color for the two pixels it needs to display.

I got GPT-5 to vibe code up a show_image.py terminal command which resizes the provided image to fit the width and height of the current terminal and displays it using Rich Pixels. That script is here, you can run it with uv like this:

uv run https://tools.simonwillison.net/python/show_image.py \
  image.jpg

Here's what I got when I ran it against my V&A East Storehouse photo from this post:

# 2nd September 2025, 11:05 am / ascii-art, cli, python, unicode, ai, generative-ai, llms, uv, vibe-coding, gpt-5, rich, gpt

The perils of vibe coding. I was interviewed by Elaine Moore for this opinion piece in the Financial Times, which ended up in the print edition of the paper too! I picked up a copy yesterday:

From the article, with links added by me to relevant projects:

Willison thinks the best way to see what a new model can do is to ask for something unusual. He likes to request an SVG (an image made out of lines described with code) of a pelican on a bike and asks it to remember the chickens in his garden by name. Results can be bizarre. One model ignored his prompts in favour of composing a poem.

Still, his adventures in vibe coding sound like an advert for the sector. He used Anthropic's Claude Code, the favoured model for developers, to make an OCR (optical character recognition - software loves acronyms) tool that will copy and paste text from a screenshot.

He wrote software that summarises blog comments and has plans to build a custom tool that will alert him when a whale is visible from his Pacific coast home. All this by typing prompts in English.

I've been talking about that whale spotting project for far too long. Now that it's been in the FT I really need to build it.

(On the subject of OCR... I tried extracting the text from the above image using GPT-5 and got a surprisingly bad result full of hallucinated details. Claude Opus 4.1 did a lot better but still made some mistakes. Gemini 2.5 did much better.)

# 29th August 2025, 5:51 pm / ocr, ai, generative-ai, llms, claude, vibe-coding, press-quotes, gpt-5, gpt

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.

# 15th August 2025, 11:09 pm / ai, openai, prompt-engineering, generative-ai, llms, system-prompts, gpt-5, gpt

OK, I can partly explain the LLM chess weirdness now (via) Last week Dynomight published Something weird is happening with LLMs and chess pointing out that most LLMs are terrible chess players with the exception of gpt-3.5-turbo-instruct (OpenAI's last remaining completion as opposed to chat model, which they describe as "Similar capabilities as GPT-3 era models").

After diving deep into this, Dynomight now has a theory. It's mainly about completion models v.s. chat models - a completion model like gpt-3.5-turbo-instruct naturally outputs good next-turn suggestions, but something about reformatting that challenge as a chat conversation dramatically reduces the quality of the results.

Through extensive prompt engineering Dynomight got results out of GPT-4o that were almost as good as the 3.5 instruct model. The two tricks that had the biggest impact:

1. Examples. Including just three examples of inputs (with valid chess moves) and expected outputs gave a huge boost in performance.
2. "Regurgitation" - encouraging the model to repeat the entire sequence of previous moves before outputting the next move, as a way to help it reconstruct its context regarding the state of the board.

They experimented a bit with fine-tuning too, but I found their results from prompt engineering more convincing.

No non-OpenAI models have exhibited any talents for chess at all yet. I think that's explained by the A.2 Chess Puzzles section of OpenAI's December 2023 paper Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision:

The GPT-4 pretraining dataset included chess games in the format of move sequence known as Portable Game Notation (PGN). We note that only games with players of Elo 1800 or higher were included in pretraining.

# 21st November 2024, 7:51 pm / ai, openai, prompt-engineering, generative-ai, gpt-4, llms, training-data, gpt

Prompt GPT-4o audio. A week and a half ago I built a tool for experimenting with OpenAI's new audio input. I just put together the other side of that, for experimenting with audio output.

Once you've provided an API key (which is saved in localStorage) you can use this to prompt the gpt-4o-audio-preview model with a system and regular prompt and select a voice for the response.

I built it with assistance from Claude: initial app, adding system prompt support.

You can preview and download the resulting wav file, and you can also copy out the raw JSON. If you save that in a Gist you can then feed its Gist ID to https://tools.simonwillison.net/gpt-4o-audio-player?gist=GIST_ID_HERE (Claude transcript) to play it back again.

You can try using that to listen to my French accented pelican description.

There's something really interesting to me here about this form of application which exists entirely as HTML and JavaScript that uses CORS to talk to various APIs. GitHub's Gist API is accessible via CORS too, so it wouldn't take much more work to add a "save" button which writes out a new Gist after prompting for a personal access token. I prototyped that a bit here.

# 28th October 2024, 4:38 am / audio, github, javascript, tools, ai, openai, generative-ai, gpt-4, llms, ai-assisted-programming, claude, claude-artifacts, claude-3-5-sonnet, cors, multi-modal-output, prompt-to-app, gpt

My @covidsewage bot now includes useful alt text. I've been running a @covidsewage Mastodon bot for a while now, posting daily screenshots (taken with shot-scraper) of the Santa Clara County COVID in wastewater dashboard.

Prior to today the screenshot was accompanied by the decidedly unhelpful alt text "Screenshot of the latest Covid charts".

I finally fixed that today, closing issue #2 more than two years after I first opened it.

The screenshot is of a Microsoft Power BI dashboard. I hoped I could scrape the key information out of it using JavaScript, but the weirdness of their DOM proved insurmountable.

Instead, I'm using GPT-4o - specifically, this Python code (run using a python -c block in the GitHub Actions YAML file):

import base64, openai
client = openai.OpenAI()
with open('/tmp/covid.png', 'rb') as image_file:
    encoded_image = base64.b64encode(image_file.read()).decode('utf-8')
messages = [
    {'role': 'system',
     'content': 'Return the concentration levels in the sewersheds - single paragraph, no markdown'},
    {'role': 'user', 'content': [
        {'type': 'image_url', 'image_url': {
            'url': 'data:image/png;base64,' + encoded_image
        }}
    ]}
]
completion = client.chat.completions.create(model='gpt-4o', messages=messages)
print(completion.choices[0].message.content)

I'm base64 encoding the screenshot and sending it with this system prompt:

Return the concentration levels in the sewersheds - single paragraph, no markdown

Given this input image:

Here's the text that comes back:

The concentration levels of SARS-CoV-2 in the sewersheds from collected samples are as follows: San Jose Sewershed has a high concentration, Palo Alto Sewershed has a high concentration, Sunnyvale Sewershed has a high concentration, and Gilroy Sewershed has a medium concentration.

The full implementation can be found in the GitHub Actions workflow, which runs on a schedule at 7am Pacific time every day.

# 25th August 2024, 4:09 pm / accessibility, alt-text, projects, ai, covid19, shot-scraper, openai, generative-ai, gpt-4, llms, gpt

Using gpt-4o-mini as a reranker. Tip from David Zhang: "using gpt-4-mini as a reranker gives you better results, and now with strict mode it's just as reliable as any other reranker model".

David's code here demonstrates the Vercel AI SDK for TypeScript, and its support for structured data using Zod schemas.

const res = await generateObject({
  model: gpt4MiniModel,
  prompt: `Given the list of search results, produce an array of scores measuring the liklihood of the search result containing information that would be useful for a report on the following objective: ${objective}\n\nHere are the search results:\n<results>\n${resultsString}\n</results>`,
  system: systemMessage(),
  schema: z.object({
    scores: z
      .object({
        reason: z
          .string()
          .describe(
            'Think step by step, describe your reasoning for choosing this score.',
          ),
        id: z.string().describe('The id of the search result.'),
        score: z
          .enum(['low', 'medium', 'high'])
          .describe(
            'Score of relevancy of the result, should be low, medium, or high.',
          ),
      })
      .array()
      .describe(
        'An array of scores. Make sure to give a score to all ${results.length} results.',
      ),
  }),
});

It's using the trick where you request a reason key prior to the score, in order to implement chain-of-thought - see also Matt Webb's Braggoscope Prompts.

# 11th August 2024, 6:06 pm / ai, prompt-engineering, generative-ai, gpt-4, llms, ai-assisted-search, gpt

CalcGPT (via) Fun satirical GPT-powered calculator demo by Calvin Liang, originally built in July 2023. From the ChatGPT-generated artist statement:

The piece invites us to reflect on the necessity and relevance of AI in every aspect of our lives as opposed to its prevailing use as a mere marketing gimmick. With its delightful slowness and propensity for computational errors, CalcGPT elicits mirth while urging us to question our zealous indulgence in all things AI.

The source code shows that it's using babbage-002 (a GPT3-era OpenAI model which I hadn't realized was still available through their API) that takes a completion-style prompt, which Calvin primes with some examples before including the user's entered expression from the calculator:

1+1=2
5-2=3
2*4=8
9/3=3
10/3=3.33333333333
${math}=

It sets \n as the stop sequence.

# 28th July 2024, 4:40 pm / ai, gpt-3, openai, generative-ai, llms, gpt

Accidental GPT-4o voice preview (via) Reddit user RozziTheCreator was one of a small group who were accidentally granted access to the new multimodal GPT-4o audio voice feature. They captured this video of it telling them a spooky story, complete with thunder sound effects added to the background and in a very realistic voice that clearly wasn't the one from the 4o demo that sounded similar to Scarlet Johansson.

OpenAI provided a comment for this Tom's Guide story confirming the accidental rollout so I don't think this is a faked video.

# 28th June 2024, 8:53 pm / ai, openai, generative-ai, chatgpt, gpt-4, gpt

A Picture is Worth 170 Tokens: How Does GPT-4o Encode Images? (via) Oran Looney dives into the question of how GPT-4o tokenizes images - an image "costs" just 170 tokens, despite being able to include more text than could be encoded in that many tokens by the standard tokenizer.

There are some really neat tricks in here. I particularly like the experimental validation section where Oran creates 5x5 (and larger) grids of coloured icons and asks GPT-4o to return a JSON matrix of icon descriptions. This works perfectly at 5x5, gets 38/49 for 7x7 and completely fails at 13x13.

I'm not convinced by the idea that GPT-4o runs standard OCR such as Tesseract to enhance its ability to interpret text, but I would love to understand more about how this all works. I imagine a lot can be learned from looking at how openly licensed vision models such as LLaVA work, but I've not tried to understand that myself yet.

# 7th June 2024, 11:30 pm / ai, openai, generative-ai, gpt-4, llms, tokenization, gpt

Extracting Concepts from GPT-4. A few weeks ago Anthropic announced they had extracted millions of understandable features from their Claude 3 Sonnet model.

Today OpenAI are announcing a similar result against GPT-4:

We used new scalable methods to decompose GPT-4’s internal representations into 16 million oft-interpretable patterns.

These features are "patterns of activity that we hope are human interpretable". The release includes code and a paper, Scaling and evaluating sparse autoencoders paper (PDF) which credits nine authors, two of whom - Ilya Sutskever and Jan Leike - are high profile figures that left OpenAI within the past month.

The most fun part of this release is the interactive tool for exploring features. This highlights some interesting features on the homepage, or you can hit the "I'm feeling lucky" button to bounce to a random feature. The most interesting I've found so far is feature 5140 which seems to combine God's approval, telling your doctor about your prescriptions and information passed to the Admiralty.

This note shown on the explorer is interesting:

Only 65536 features available. Activations shown on The Pile (uncopyrighted) instead of our internal training dataset.

Here's the full Pile Uncopyrighted, which I hadn't seen before. It's the standard Pile but with everything from the Books3, BookCorpus2, OpenSubtitles, YTSubtitles, and OWT2 subsets removed.

# 6th June 2024, 8:54 pm / ai, openai, generative-ai, gpt-4, llms, interpretability, training-data, gpt

Hello GPT-4o. OpenAI announced a new model today: GPT-4o, where the o stands for "omni".

It looks like this is the gpt2-chatbot we've been seeing in the Chat Arena the past few weeks.

GPT-4o doesn't seem to be a huge leap ahead of GPT-4 in terms of "intelligence" - whatever that might mean - but it has a bunch of interesting new characteristics.

First, it's multi-modal across text, images and audio as well. The audio demos from this morning's launch were extremely impressive.

ChatGPT's previous voice mode worked by passing audio through a speech-to-text model, then an LLM, then a text-to-speech for the output. GPT-4o does everything with the one model, reducing latency to the point where it can act as a live interpreter between people speaking in two different languages. It also has the ability to interpret tone of voice, and has much more control over the voice and intonation it uses in response.

It's very science fiction, and has hints of uncanny valley. I can't wait to try it out - it should be rolling out to the various OpenAI apps "in the coming weeks".

Meanwhile the new model itself is already available for text and image inputs via the API and in the Playground interface, as model ID "gpt-4o" or "gpt-4o-2024-05-13". My first impressions are that it feels notably faster than gpt-4-turbo.

This announcement post also includes examples of image output from the new model. It looks like they may have taken big steps forward in two key areas of image generation: output of text (the "Poetic typography" examples) and maintaining consistent characters across multiple prompts (the "Character design - Geary the robot" example).

The size of the vocabulary of the tokenizer - effectively the number of unique integers used to represent text - has increased to ~200,000 from ~100,000 for GPT-4 and GPT-3.5. Inputs in Gujarati use 4.4x fewer tokens, Japanese uses 1.4x fewer, Spanish uses 1.1x fewer. Previously languages other than English paid a material penalty in terms of how much text could fit into a prompt, it's good to see that effect being reduced.

Also notable: the price. OpenAI claim a 50% price reduction compared to GPT-4 Turbo. Conveniently, gpt-4o costs exactly 10x gpt-3.5: 4o is $5/million input tokens and $15/million output tokens. 3.5 is $0.50/million input tokens and $1.50/million output tokens.

(I was a little surprised not to see a price decrease there to better compete with the less expensive Claude 3 Haiku.)

The price drop is particularly notable because OpenAI are promising to make this model available to free ChatGPT users as well - the first time they've directly made their "best" model available to non-paying customers.

Tucked away right at the end of the post:

We plan to launch support for GPT-4o's new audio and video capabilities to a small group of trusted partners in the API in the coming weeks.

I'm looking forward to learning more about these video capabilities, which were hinted at by some of the live demos in this morning's presentation.

# 13th May 2024, 7:09 pm / ai, openai, generative-ai, gpt-4, llms, vision-llms, llm-pricing, multi-modal-output, chatbot-arena, gpt

Extracting data from unstructured text and images with Datasette and GPT-4 Turbo. Datasette Extract is a new Datasette plugin that uses GPT-4 Turbo (released to general availability today) and GPT-4 Vision to extract structured data from unstructured text and images.

I put together a video demo of the plugin in action today, and posted it to the Datasette Cloud blog along with screenshots and a tutorial describing how to use it.

# 9th April 2024, 11:03 pm / projects, ai, datasette, datasette-cloud, openai, generative-ai, gpt-4, llms, vision-llms, structured-extraction, gpt

“The king is dead”—Claude 3 surpasses GPT-4 on Chatbot Arena for the first time. I’m quoted in this piece by Benj Edwards for Ars Technica:

“For the first time, the best available models—Opus for advanced tasks, Haiku for cost and efficiency—are from a vendor that isn’t OpenAI. That’s reassuring—we all benefit from a diversity of top vendors in this space. But GPT-4 is over a year old at this point, and it took that year for anyone else to catch up.”

# 27th March 2024, 4:58 pm / ai, openai, generative-ai, gpt-4, llms, anthropic, claude, benj-edwards, chatbot-arena, press-quotes, gpt

Inflection-2.5: meet the world’s best personal AI (via) I’ve not been paying much attention to Inflection’s Pi since it released last year, but yesterday they released a new version that they claim is competitive with GPT-4.

“Inflection-2.5 approaches GPT-4’s performance, but used only 40% of the amount of compute for training.”

(I wasn’t aware that the compute used to train GPT-4 was public knowledge.)

If this holds true, that means that the GPT-4 barrier has been well and truly smashed: we now have Claude 3 Opus, Gemini 1.5, Mistral Large and Inflection-2.5 in the same class as GPT-4, up from zero contenders just a month ago.

# 8th March 2024, 12:51 am / ai, generative-ai, gpt-4, llms, llm-release, gpt