Yann Leflour

Yann Leflour

@yleflour.me

Engineer and maker at 💜 Exploring software craftsmanship with AI

81 followers225 following172 posts

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LLMs can't synthesize information and nobody's talking about it I was looking into Yoji Akao's book on Quality Function Deployment last week. 1st thing I noticed: half the pages are charts Guess what it takes for an LLM to convey the same level of information as a single one of these charts?

Ethan Mollick@emollick.bsky.social

An OpenAI researcher (Noam Brown) was generally mocked last month for suggesting that even air-gapped AI models on separate computers would find a way to communicate with each other, perhaps through CPU temperature changes. Given this latest report from OpenAI, it doesn’t seem so implausible.

While we keep trashing on slop code, let's remember that this is the experience plenty users still have to deal with Sure LLMs can't do everything, but today's models are likely to spot these issues & display proper errors without extra instructions

Bruce Lawson@brucel.bsky.social

I have found the Worst Website in the World. It is the official Indian Railways ticket booking site. After the 96 step sign up process, it randomly throws you out; it requires repeated entry of information it already has, and occasionally throws useless error messages. Wanted 2 seats, now have 4.

That was my main pain point of 2024. I kept developping custom tools to fill in the gap and try to consolidate into a product Every new model/harness update filled part of the gap and made my tools obsolete I quit trying to improve the "code writing" part around that time

dax@thdxr.com

a weird inversion with LLMs is the models improve faster than the tinkerers when i see people with custom workflows and setups they're all addressing problems that don't exist anymore the person naively using vanilla codex is more likely to be experiencing state of the art

Now my top priority is - Making legacy codebases AI ready through reverse engineering pipelines - Keeping the overall product legible to the product teams with graphs, schemas, ... as most teams struggle at keeping track of the changes This part hasn't changed since GPT 3 release

LLMs can't synthesize information and nobody's talking about it I was looking into Yoji Akao's book on Quality Function Deployment last week. 1st thing I noticed: half the pages are charts Guess what it takes for an LLM to convey the same level of information as a single one of these charts?

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We've taught these models to write, to code, to reason out loud. We haven't taught them to draw, to take a mess of relationships and compress it into one thing you can see Until we do, "synthesis" stays a human skill. And I'm not sure the labs are even pointing at it

LLMs can't synthesize information and nobody's talking about it I was looking into Yoji Akao's book on Quality Function Deployment last week. 1st thing I noticed: half the pages are charts Guess what it takes for an LLM to convey the same level of information as a single one of these charts?

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I draw to explain concepts, problems, solutions to my team almost every day A freeform sketch settles an argument faster than any documentation I've written. I'll go further: knowing how to draw is a core engineering skill, and I think we should teach it the way we teach coding

It's also exactly what LLMs can't do. Ask one for a diagram and you'll see it. It produces a picture of a chart, not a chart that does the thinking for you So here's what I keep coming back to

LLMs can't synthesize information and nobody's talking about it I was looking into Yoji Akao's book on Quality Function Deployment last week. 1st thing I noticed: half the pages are charts Guess what it takes for an LLM to convey the same level of information as a single one of these charts?

I ran the numbers. For one A3-size chart, the LLM wrote down: - 8 sections - 33 paragraphs - 1,048 words - 7 A4 pages for the same level of information

A person can scan the chart in about 30 seconds and you can kickstart a team discussion around the chart in a matter of minutes Meanwhile, the LLM-produced document requires the entire team to go through the 7 pages and mentally map the related information before anything

Superpowers is an amazing piece of engineering It may seem like a simple brainstorm / plan / execute worflow, but the execution is top notch If you care about software craftsmanship, then it's a must have in your toolset The output's quality for each step is unparalled github.com/obra/superp...

github.comGitHub - obra/superpowers: An agentic skills framework & software development methodology that works.An agentic skills framework & software development methodology that works. - obra/superpowers

3. I've invested in learning how to use this tool in my field of expertise. Holy cow, it's transformational but will be a plague for human intent, knowledge, and creativity.

Justin Wolfers@justinwolfers.bsky.social

There are basically only two positions in the debate about AI. 1. I’ve barely invested any time in learning how to use it effectively. AI sucks. 2. I’ve invested in learning how to use this tool. Holy cow, it’s transformational. Position #1 has lower barriers to entry.

It's July 2025 and prompting is still a skill A skill I have a hard time teaching Some people just get it, but most struggle And no, LLMs are not very good at prompting themselves without another prompt to guide them

Cursor has a system prompt issue. I'm using LLMs for a complex software migrations and have leveraged everything I know about prompting. While the model is the same as Claude Code, Cursor seems unable to follow the provided instructions. Cursor simply feels broken now

When I'm hired to enable a team with AI tools what I'm actually paid for is asking the right questions to reverse engineer how the team works When I have that, building the tools is the easy part Also why I fear that the agents trend will do a lot of harm to craftsmanship

If you're building LLM based products, check out napkin.ai Blending pre-made templates with LLM formatting may seem like a straightforward idea but they've executed it really well with a clean UX And if you need to fill a presentation with corporate BS, just ask it to generate the text as well 😅

An upside of LLMs is that, just by using them, people will get better at providing instructions and the right context The downside is that they'll likely stay to get frustrated when you take over 1mn to respond

While illustrating the many disciplines connected to AI and LLMs I realized that I don't have a term for UX Designers / PMs focused on AI Really shows how under-explored UX for AI is I agree it's hard, but we could be doing so much more than chats

Has anyone studied the impact of image and sound on multi-modal LLMs ? Like if you're working with the LLM, does passing it classical music unrelated to the task influences its response ? What about art pieces ?

This also means that Deepseek R1 will be a potential attack vector. Willingly put inside a state system Imagine if they trained the model to act maliciously when it figures out that it's being used inside a targeted infrastructure I wonder what it could realistically achieve

Michael Kevin Spencer@aisupremacy.bsky.social

India lauds Chinese AI lab DeepSeek, plans to host its models on local servers. The approval to allow DeepSeek to be hosted in India appears contingent on the platform storing and processing all Indian users’ data domestically, in line with India’s strict data localization requirements.

"Gemini 2 Flash Thinking" is a very impressive model I'm having discussions with it about high level and abstract concepts and it just "gets it" The 1M tokens context also means that you can have extremely long conversations with high retrieval rates and without running out of chat history

Quick PSA to every developer applying for their first job: It's time to include LLM tools in your resume (Copilot, Cursor, ...) along with any expirement you've done with them I'm currently reviewing resumes and none mention these, so it's still a good way to set your profile appart

I'm also asking candidates to provide me with links to some chats with ChatGPT (or equivalent) in order to assess how they work with LLMs My guess is that this will become a common practice in a near future

I stopped my #devin (yes the "AI Engineer") subscription, felt too uneasy about merging those pull requests It was a fun (though way too expensive) trial It's hard to put it, but to me it's like most "AI art", the demo is impressive but it clearly lacks any intent

And I believe intent is crucial when developping. I'd rather have a human one I can train and steer than this husk of a developper Copilots and specific agents are not the same, they're like advanced paintbrush, helping you with the first draft or tiny touches. So I'll stick with those for now

@vercel.com's AI SDK is amazing to work with But I don't get why I had to implement a middleware to add the last message from the prompt to the result if its role is "assistant" Should be a default behavior Anyway, if you need it it's "includeAssistantMessage" in @synstack/llm/middleware

Even though I would not be surprised to see something like this implemented, there is no way it's true But seeing how this is spreading, it shows that we're far from achieving widespread understanding of what AI really is

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4️⃣ Prompt chaining Because prompt scripting is mostly async, I disliked writing await and Promise.all to whenever working on message arrays So, the message() param can be a Promise<Array<Message>> or an Array<Promise<Message>>, making it simpler and cleaner to build out your prompt and chain agents

There are more libraries I've built @ www.npmjs.com/org/synstack Make sure to check out "@synstack/fs" which the other one I'm most proud of I've been using them over the last 6 months to help teams succesfully triple their velocity during software migrations and they'll keep on getting better

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2️⃣ Type safety There's type safety at every level: 🔶 Tool definitions, filters, & outputs 🔶 Calling the llm with missing required parameters will trigger a TS error 🔶 Message template string values are type-safe as well Even my test coverage is mostly type testing for now github.com/pAIrprogio/...

github.comsynscript/packages/llm/src/completion.builder.test.ts at main · pAIrprogio/synscriptLibraries for immutable and chainable LLM compatible scripting - pAIrprogio/synscript

3️⃣ Template strings I hate the base JS template string because it breaks indentation So I wrote a separate library to fix that. 🔶 Trims text and removes extra indentation 🔶 Auto-resolves promises 🔶 Arrays to separate lines 🔶 Propagates nested indentation github.com/pAIrprogio/...

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This allows me to benefit from the countless integrations and tons of contributions to AI SDK while keeping my target level of developer experience Here's a quick tour of some of the features:

1️⃣ Immutability + Chaining I've always been a fan of superagent's clean immutable and chainable API allowing me to build upon a base instance of query github.com/ladjs/super... This was my target to replicate, but for LLMs and most of my libraries published under @synstack operate that way

github.comGitHub - ladjs/superagent: Ajax for Node.js and browsers (JS HTTP client). Maintained for @forwardemail, @ladjs, @spamscanner, @breejs, @cabinjs, and @lassjs.Ajax for Node.js and browsers (JS HTTP client). Maintained for @forwardemail, @ladjs, @spamscanner, @breejs, @cabinjs, and @lassjs. - ladjs/superagent

For v1, I wrote my own abstraction layer While I love the chainable + immutable + string-templates + type-safety combo, writing my own adapters was too time consuming So after using @mattpocock.com Evalite, I decided to keep my approach but switch the engine to Vercel's sdk as a peer dependency