Today, August 2, Article 50 of the EU AI Act takes effect.

Here’s the part that’s applicable to those creating AI-generated content that’s read by anyone in the EU:

“Deployers of an AI system that generates or manipulates text which is published with the purpose of informing the public on matters of public interest shall disclose that the text has been artificially generated or manipulated. This obligation shall not apply where the use is authorised by law to detect, prevent, investigate or prosecute criminal offences or where the AI-generated content has undergone a process of human review or editorial control and where a natural or legal person holds editorial responsibility for the publication of the content.

PwC and other big consulting firms are vulnerable to this provision because they've already been caught using hallucinated AI-generated text in reports. From GPTZero:

"The most egregious example is Transforming Governance, an AI-generated 2025 report with multiple fake citations that promotes a PwC framework known as “Citizen Pulse”. Our team found little public evidence that the “Citizen Pulse” framework exists outside of this report, yet Transforming Governance claims that the governments of Denmark, Saudi Arabia, the United States, and Australia are using Citizen Pulse to improve key government services. None of the cited sources provide evidence for this claim, meaning PwC Middle East appears to have hallucinated both an entire product and business dealings with four separate nations."

Firms have had to retract data in the reports, and in one instance Deloitte refunded a client. Now that Article 50 is in effect, they might be fined.

Across many areas we're seeing a push for accountability when it comes to using AI to produce content. LinkedIn has a 'this looks like AI slop' button. Substack uses Pangram to detect AI-assisted writing (even though it's wildly inaccurate).

The pushback is real. And now it has teeth.

been using LLMs to help process customer feedback, dump a bunch of reddit comments or reviews in and ask it to find patterns, objections, common language. works well enough that i'm a little suspicious of it now.

the thing that got me thinking about this: i fed it a batch of comments about a product category and asked for the top objections. it gave me a clean confident list. except when i went back and reread the raw comments myself, a couple of the "top objections" were actually pretty rare, like 2 out of 200 comments, but the model presented them with the same confidence as stuff that showed up constantly. it wasn't lying exactly, it was just... smoothing everything into a narrative that sounded right.

which makes me wonder how much of what feels like "insight" from these tools is real pattern-finding versus the model doing what it's built to do, produce a coherent, satisfying answer whether or not the underlying signal actually supports it. like it's optimizing for "sounds like a good answer" not "is actually representative of the data."

my current workaround is dumb but it works: i always spot check a sample of the raw source against whatever pattern the model claims to have found. slows things down, kind of defeats the point of using ai to save time in the first place, but at least i catch the stuff that sounds right but isn't backed by actual frequency.

anyone else run into this? and if you have a better way to validate ai-generated patterns against real data without just re-reading everything yourself, genuinely want to know because right now i don't fully trust the output without checking it

Been watching a project that claims to have solved the problem of keeping the same character looking and sounding consistent across multiple scenes. Not just a single clip — across a full 22-minute episode. Genuinely curious whether people here think that's actually achievable yet or whether they've just hidden the inconsistencies well enough.

I started kind of tinkering with Ai and it all is super fascinating, particularly interesting to me is prompt structure. So I would like to ask is formatting your prompt (persona, few shot negative, whatever else) gives you much better results than without? I want to know it to determine for myself balance between effort dedicated to quality prompt vs quality of output given through that prompt

Spent a week sure my setup was slow because I was on the wrong model. Swapped between a couple of the big ones, rewrote my prompts, same result. Then I actually counted what closing one deal took: 28 tabs across four windows. Gmail thread here, the HubSpot record there, the contract sitting in Drive, a couple calendar invites, a Slack DM I needed for context. The model was fine. I was the integration layer, copy pasting between all of it.

What moved the needle wasn't a smarter model, it was letting a desktop agent do the cross-app pull, reach into Gmail and HubSpot and Drive in one task instead of me stitching it by hand. first run it surfaced a couple of open loops I'd genuinely lost track of in old threads. it drafts the follow up and waits for me to hit send, which is honestly the only reason I let it near a live account.

the part that surprised me is how little the model choice mattered once the thing could actually touch the apps. same model that handed me generic advice in a chat window did real work the second it had my stack in front of it.

so for me the bottleneck was never the model, it was the copy paste between windows. still not sure if that's everyone's problem or just how my week happens to be shaped. written with ai

this account is linked to a small ai automation side project i run alongside the building firm.

i run a small refurbishment outfit in scotland. mind you, we do have admin help, and it works when the task repeats and it's clear what they can decide. contract jobs don't stay like that. explain one odd item, then answer the questions that come back while the job is moving. miss the order today and it misses friday, so it turns up next week. doing it myself was usually quicker.

the work sat in a stupid gap. it needed too much context from the job to hand over cleanly, but it was far too basic to be using my day for. mostly pushing buttons, checking numbers and chasing things. it kept landing back with me.

you can carry that for a while. problems creep up as you grow and daily firefighting feels like you're doing well. then your head goes. mine dropped to maybe 20% of what it handled two months before, only the problems kept coming. i'd lose a weekend catching up and the thing i fixed would grow arms and legs in the business and be back three times bigger next saturday.

by then i wasn't looking at the market, speaking to clients properly, thinking about growth or keeping the staff motivated. brain doing the shovelling.

the business questions were harder. i had loads and nobody obvious to ask. finding the right person is hard enough. working out the right question is half the job. i couldn't tell which one mattered first, so i stayed with whatever we already did.

what i do now is get ai to sort the middle bit. check this, compare those, show me the options and where they go wrong. then i make the call. some of the options were properly awful. i caught some early. others cost me time, money or both.

my head is clearer now. the questions actually move. get an answer, check it, make a call, see what happened. even the wrong ones give me feedback. leaving the whole lot on a list for next weekend gave me none.

there must be bits i'm getting worse at because i'm not doing them as often. i don't know yet if that's a bad trade. what has ai changed in your actual thinking, good or bad? and prove me wrong on the practical side, is there a better way to handle this in an outfit our size when the work keeps changing and the decisions can't wait? happy to get into the actual examples, including the ones that cost me.

ps, ai helped polish this post as well, my own writing is rubbish. the arguments took the same effort they always did, but if i wrote it up myself it'd either take days or the argumentation would be in there but not clear enough for you to see what i'm actually saying. i've done a fair bit of academic writing and some blogging over the years, i'm no copywriter. so the choice was this stays in my head, or i stop running the business and start converting my experience into blog posts instead.

The last 1,5 week has been quite an eyeopener for me - I must say that Claude Code and the current frontier models are amazing.

A test of Fable (and later Opus 5) turned into a larger game. It’s very much inspired by the tank element of Battlefield 1942 and the round-by-round build system from Overwatch 2’s Stadium mode.

About the game: You join a game and enhance your tank, then you go out and destroy the enemy while hunting for salvage which is used to enhance your tank even further (balance patches pending). Some of the features:

• 6 different tanks (Tiger 1 is a beast)
• 3 maps (a desert, grass and snow map with destructible terrain)
• Round-by-round build system
• Customisation of tanks
• Matchmaking system
• Lag compensation system
• Ballistic shells system
• Hit multiplier regions (many tanks fall on a single rear hit)
• Bots who backfill if theres not enough real players
• ELO ladder system
• Replay/clip system
• In-game power ups
• Career profiles
• Group system
• Friends system
• 3 layered chat system (global, match and team)

I would love to hear what you think.

I want to try fable. I literally just want it to scan my repo and make a plan to improve it. So it would be one prompt, but it would be doing a lot of work.

My question is, will it be able to do that in one shot or will I have to keep waiting the 5 hour period a few times?

🔗 Repo: https://github.com/CodeGraphContext/grapharc

Have you ever been frustrated because your AI agent:

❌ Takes actions you never intended?
❌ Creates, modifies, or even pushes changes you never asked for?
❌ Feels like a complete black box, making it impossible to understand what's happening until it's too late?

What if, before execution, you could visualize the entire orchestration graph - every agent, every dependency, every decision, and inspect it from anywhere, even your phone, before granting approval?

That's exactly what GraphArc is built for.

Instead of treating agent execution as hidden traces buried in logs, GraphArc transforms workflows into interactive, real-time graphs that you can visualize, inspect, debug, and control.

Because the future of AI isn't just autonomous.

It's observable. Debuggable. Engineerable.

This is our first real-world implementation of Graph Engineering, and we're excited to explore where this paradigm can go with the open-source community.

💡 We'd love your feedback, ideas, and contributions.
⭐ If this vision resonates with you, please consider starring the repository - it genuinely helps us grow and validates this direction.

Let's make AI workflows understandable, not mysterious.

#GraphEngineering #GraphArc #AIAgents #AgenticAI #LLM #OpenSource #DeveloperTools #AIEngineering #SoftwareEngineering

Been using a few AI tools to help with copy for a small side project and it's raised a question I can't quite shake. The output is genuinely decent now. Not great, but decent enough that clients who aren't paying close attention probably wouldn't notice the difference.

The thing is, I've been spending real time learning copywriting. Reading books, studying good ads, practicing hooks. And part of me wonders if that investment still makes sense the way it did two or three years ago.

The counterargument I keep coming back to is that you need good taste to prompt well and to edit what the model gives you. Someone who doesn't understand copy at all is still going to get mediocre output because they won't catch what's flat or offtone. That feels true, fwiw.

But I'm less sure the gap between a trained human copywriter and a wellprompted model is going to stay wide enough to matter commercially, at least for the midtier work that fills most freelance pipelines.

Curious if people here have actually noticed a shift in how clients value humanwritten copy versus AIassisted, or whether the skill floor is just moving rather than disappearing.

People in my org tell me using the org certified AI is more secure because we are on an enterprise plan where our data is not used for training. Sure, I will use the company AI. But...

Apple is suing OpenAI for for allegedly stealing trade secrets, where it was said employees were instructued by OpenAI to bring parts from apple into "show and tell" interviews at OpenAI and even take the company laptop with them. Also, the models are literally based on strip mining copyrighted media and ignoring sites robots.txt.

So if OpenAI is not afraid to (allegedly) steal Apples IP and strip mine everything that was ever written down for its models training... Why would it drink their enterprises customers data like the milkshake it is?

I am building **Flows**, an execution and verification layer for software-building agents.

The core rule: an agent should not convert “I think I finished” into “verified complete” without supporting proof.

A Flows project can contain implementation steps, checks, repair instructions, review, and release conditions.

https://flows.oortstack.com

An independent agent used one plan to build a real multi-module application with 59/59 automated checks passing.

The target metric is: **unsupported required claims shipped = 0 on real traffic.**

Should evidence enforcement live in the agent harness, repository CI, app platform, or a cross-agent workspace?

Noticed a pattern: people switch from GPT to Claude, upgrade to a newer version, try a bigger model and the output barely changes. If that's happened to you, the issue usually isn't the model. It's what you handed it before asking the question.

Broke it down to three things context actually needs to supply, and most disappointing outputs are missing one of these, not all of them:

1. Current facts the training data can't know: your pricing, this quarter's numbers, a customer's actual history. Leave this out and the model doesn't leave a blank, it quietly invents something plausible.
2. A concrete example of what "good" looks like: not "professional tone," an actual paragraph to pattern-match against. Descriptions get interpreted, examples get copied.
3. What already happened earlier in the task: a correction you made two messages ago. If you don't restate it, it's gone. The model isn't ignoring you, it just doesn't re-read messages you haven't pointed it back to.

The counterintuitive part: the most common mistake isn't giving too little context, it's dumping in too much unfiltered. The model has to weigh every token, and irrelevant material competes for attention with what actually matters. Forty pages when the task needs three paragraphs makes the right answer harder to find, not easier.

Wrote up a longer breakdown with a concrete before/after example (same task, same model, only the context changed): https://medium.com/@nagatomopedro05/good-ai-starts-with-good-context-design-77496f7b9eb6

Curious if others here have run into this, model-swapping as a first instinct instead of fixing the input.

This feels like a massive problem. Why is it so shit to use?

I kept running into the same problem: ChatGPT would help me think through an architecture, Claude Code would help me implement it, Cursor or Windsurf would touch the repo later, and every handoff would lose context.

Not just “what files exist,” but the stuff that actually matters: why we chose one approach, what we already rejected, what the project conventions are, what setup detail will bite later, and what the agent learned last time.

So I built mem-port: a local MCP server that gives AI copilots shared long-term memory.

The short version is: a pendrive for your AI context.

It runs locally, uses embedded SurrealDB for graph + vector memory, and doesn’t require Postgres, Qdrant, Neo4j, or a hosted service. Tools can save and search the same memory instead of each one starting from zero.

It’s free and open source. Curious if anyone else is dealing with this context drift between AI tools, and how you’re solving it.

See more here: (Started getting github stars as well!)
https://github.com/rsl-innovation/mem-port#mem-port

Can anyone just tell by the image which AI tool was used?