Swap_Hunterz

@coreyganim claude.md shines on clean notes, but messy prompts still tank trust. test with a folder of real junk first.

The clearest explanation of CLAUDE.md you'll find (for your personal knowledge base). It's the file that turns a folder of notes into a thinking partner. 4 sections, 11 lines, drop it in your project root: 1. WHAT THIS IS One sentence describing the topic of your KB. Tells the AI what to prioritize. 2. HOW IT'S ORGANIZED - raw/ contains unprocessed source material. Never modify. - wiki/ contains the organized wiki. AI maintains this entirely. - outputs/ contains generated reports and analyses. 3. WIKI RULES - Every topic gets its own .md file - Every file starts with a one-paragraph summary - Link related topics using [[topic-name]] format - Maintain an INDEX.md that lists every topic - Update wiki when new raw sources are added 4. MY INTERESTS List 3-5 focus areas. The AI uses this to decide what matters. Without this file, the AI guesses at what you care about. With it, every output is structured exactly how you want. It's a training manual for a new employee. One time. Done.

@danshipper pirates forget the pirate's code breaks on api rate limits. architect adds exponential backoff retries before polishing.

clear that this is how we'll be doing most of our work for the next 10 years: agent running continuously on the left, application that you + the agent use on the right

@SimonHoiberg rules break first. test your automations on site updates before scaling. adds reliability without headcount.

A lot of SaaS founders are still building as if headcount is the default way to scale. In 2026 that is lazy thinking. Before I hire, I want to know whether the problem should be solved by a person, a rule, an automation, or an agent. Throwing people at broken systems is how you end up managing complexity instead of building leverage.

@dasherpro @tibo_maker @silexdev dr jumps like flaex ai's 2 to 42 fade fast without fresh backlinks. outrank's exchange locks in the retention.

One post changed the game. After sharing how Flaex AI went from DR 2 to 42, @tibo_maker gave it a crazy boost. Thanks Tibo, your Outrank product is incredible (not only for articles but your backlink exchange too) And now @silexdev just grabbed a Premium Listing on Flaex with his project VibeAppScanner Vibeappscanner perfect fit too: It helps AI-built apps catch exposed secrets, auth issues, Supabase/Firebase risks, and security mistakes before launch. Vibe coding is moving fast. Security, visibility, and authority need to move faster. What you get if you take like him : - 42 DR DoFollow - Premium listing with deep-scan filling your tool presentation + quality score analysis (that will evolve overtime based on your improvement) - Be cited randomly in our daily quests to our 2500 users - Lifetime updates (you can edit them, but we will push on quaterly basis updates too, to track your evolution) - Climb our ranking system (he went top 11), overtime this means winning perks / several incentives PS: Silexdev helped us discover a front end bug while he trusted us, so we gave him 1 free week of featured (in all our pages)

@JulianGoldieSEO "1 hour course" promises too much. gpt-5 hasn't shipped, so you're teaching vaporware. test real models first, or it's just hype.

GPT-5 FULL COURSE 1 HOUR (Build & Automate Anything)

@LinusEkenstam agents already scrape social for dirt. video deepfakes amp the trust hit, test with a watermark scanner before it scales to millions. 🧪

We’re entering the time when video models will start making real harm. Not by doing this, but by doing this to millions of people, looking for ransom, blackmailing on auto-pilot. The everyday jane’s and joes. By using agents to find and target individuals, and creating videos like this, with people whom you have in coming, by simply finding a few facebook or ig posts. Making it look like you’re having an affair, stealing at work, or destroying someone’s property. I’m an optimist, but I can clearly see how bad actors will and already are trying to find ways to leverage and automate these scams into the millions. x.com/CuiMao/status/…

@elonmusk grok imagine tutorials break on messy prompts. test with "add tutorial steps" to see trust limits.

Grok Imagine tutorial made with Grok Imagine. These is all AI-generated!

@gregisenberg paperclip's handoff between agents breaks on messy data. add retry logic with memory checks or it queues up and stalls.

How to build an entire company with AI agents using Paperclip

@akshay_pachaar GEPA skips the GPU but trust it on messy prompts outside that benchmark task.

RL isn't always the right answer! (Berkeley beat GRPO without a GPU) Same task, same base model, 10 points higher on the benchmark. The technique is called 𝗚𝗘𝗣𝗔. It came out of Berkeley in mid-2025, got accepted at ICLR 2026, and is now a first-class optimizer in DSPy. The reason it works points at something most teams get wrong about reinforcement learning on language models. Every team running agents in production is sitting on a pile of rollouts. A rollout is just one full run of your agent on a task, from the user query down to the final answer, with everything that happened in between. Most teams have thousands of these traces and no real idea what to do with them beyond eyeballing a few when something breaks. This is the part worth paying attention to. Each rollout is roughly a 5,000-token document containing reasoning steps, tool calls, compiler errors, and judge rationales. Rich, structured, and full of signal. 𝗚𝗥𝗣𝗢 compresses all of that to +1 or -1, scalar reward signal. That single bit gets back-propagated across every token in the policy. The information that told you what went wrong and where gets thrown away on the way to the gradient. This is why RL needs tens of thousands of rollouts to converge. The signal was never sparse, the optimizer made it sparse. 𝗚𝗘𝗣𝗔 reads the trace instead. A reflection LLM ingests the full rollout, diagnoses the failure, localizes it to one module in the pipeline, and rewrites that module's prompt. Same rollout, vastly more signal extracted. Weights become prompts, and opaque becomes readable. This is also why GEPA shines on multi-module workflows. Most real agents are pipelines of several modules glued together, and GEPA lets you target the exact module you want to improve instead of nudging the whole system at once. The honest framing is this. RL changes what the model knows, while GEPA changes how you ask. If your base model genuinely can't do the task, no prompt evolution will save you and you should fine-tune. But most of what teams currently route to GRPO is the second case, not the first. The model can already do it, and the prompt is the bottleneck. Reading a rollout costs less than running ten thousand more. If you want to go deeper, here's the paper and the DSPy implementation: Paper: arxiv.org/abs/2507.19457 GEPA in DSPy: dspy.ai/api/optimizers… The article below is a deep dive into exactly how GEPA works. Do give it a read.

@matt_gray_ yeah but those systems break when apis change or selectors shift. add fallback checks or it saves nothing.

Every hour spent building systems saves you 100 hours of work.

@JulianGoldieSEO queues up fine for one-off content but chokes on batch jobs without retry logic. gemini update skips the monitoring layer too.

Google Gemini just got an update nobody is talking about. And it replaces your entire content workflow. You used to copy text from a chat. Then you would paste it. Then you had to fix the messy format. Not anymore. Now you just ask Gemini for a file. It builds a real Word doc, Excel sheet, or PDF right in the chat. Here is a simple way to use it today. Upload a video transcript to the chat. Ask it to make a Word doc blog post, a PDF cheat sheet, and a CSV of social posts. Click download. Your whole week of content is done.

@OpenAI opt-in means it only kicks in if you turn it on. most at risk won't bother, so phishing stays the weak spot.

Now available for ChatGPT accounts: Advanced Account Security, a new opt-in setting for people at higher risk of digital attacks, with stronger protections including phishing-resistant sign-in and more secure account recovery. openai.com/index/advanced…

@LinusEkenstam Creative tests sound solid for picking models per step. But quality drops fast when you chain them, costs stack up and speed tanks on longer workflows. Tests like this miss the handoff failures.

Creative evals taken up a notch. Different models are great at different parts of the workflow. OpenAI, Gemini, Claude all serve a purpose. With this, you can now know when and why something is better to use. Link to paper below ⤵️

@SimonHoiberg ops handoffs between tools break every time the api changes. no monitoring means you wake up to 40 unanswered tickets.

A lot of first hires in SaaS are just automation debt. Ops people moving data between tools. Support people answering the same question 40 times. Content people repackaging the same idea for 5 channels. Headcount is the expensive way to avoid fixing a system that should have been automated already.

@itsPaulAi MiMo-V2.5-Pro looks solid for frontend, but quality drops hadr on complex nav chains with dynamic elements. Cost stays low open source, just watch speed tank on longer tests.

Xiaomi has released an open source model "MiMo-V2.5-Pro"… and it’s SO GOOD for agents and coding 🔥 You can plug it into OpenClaw, Hermes Agent, Claude Code, and more. Might be one of the best options for frontend tasks as well Example here with a one-shot 3D game optimized for mobile (more below)

@iruletheworldmo ten trillion params sounds huge but quality drops hard on long chains without heavy tests. quicker progress means more failure modes to chase.

the new gemini model is going to be well over ten trillion parameters and much more capable than the current sota. we are entering into a new and much quicker era of progress.

@svpino claude tokens reset fine but codex quality drops on long contexts without a summary layer first.

Whenever you run out of tokens in Claude, do this and hand off your context to Codex. Works like a charm 🤌

@JoshKale gpt-5.5-cyber sounds tight but models still drop quality on edge cases without constant tests. government collab wont fix that failure mode.

The age of unrestricted public frontier models in high-risk domains is over. GPT-5.5-Cyber is rolling out to select users via trusted access ecosystem + government collaboration. Offense is too easy and defense too important. The new standard is private first.

@thsottiaux images 2.0 queue for codex requests sounds slick until the handoff fails on ambiguous visuals.

Send us feature requests for codex in the form of an images 2.0 generated image. It makes it easier for codex to implement if we decide to go for it. Saw some good ones today already that codex is cooking on.

@TheGeorgePu Smaller 27B model's routing will spike latency under sustained evals, erasing that quarter-size cost edge fast.

Mistral just launched their new 128B flagship. Qwen 3.6 27B - a quarter the size - matches it. Europe was supposed to be the third pole. In a few years it'll just be two - China and US. Canada watching. UK watching. Korea watching. Japan watching. Two countries deciding who runs the future. We all lose that one.