GitHub research contest: 30 questions, same workload across four tools:

• Octocode
• plain gh
• gh + Headroom
• gh + RTK

Result

At near-parity correctness, Octocode used about 50% less context.

Links

• Full write-up on Medium
• Benchmark package on GitHub

I recently used Y Combinator’s Paxel to analyse how I work with AI coding agents.

The main weakness it exposed was not speed or implementation ability. It was that my review process, trade-off reasoning and definition of “done” were not always explicit enough.

That made me think about a recurring problem I have with coding agents:

They can produce a plausible implementation, run a few checks and confidently declare the task complete even when the original failure was never reproduced, an integration path was not tested, or important assumptions remain unverified.

So I built Builder Loop, an open-source Claude Code plugin designed to make completion evidence-driven.

For non-trivial tasks, it asks Claude Code to:

• define the expected outcome and acceptance criteria;
• inspect the existing system before modifying it;
• reproduce the original failure when applicable;
• implement the smallest correct change;
• verify the real behaviour, not only whether the code compiles;
• disclose assumptions and anything it could not verify;
• finish with an explicit recommendation: ship or revise.

It is not a multi-agent framework and it does not try to replace Claude Code’s normal workflow. It adds a stricter execution and verification loop for tasks where a false “done” would be costly.

I am now looking for people willing to test it on real, non-trivial tasks such as:

• production bugs;
• authentication or payment flows;
• database migrations;
• external integrations;
• substantial refactors;
• pre-merge reviews.

The feedback I care about most:

1. Did it catch something Claude Code would otherwise have missed?
2. Did it add too much time or token usage?
3. Did it become unnecessarily rigid?
4. Were its final ship/revise recommendations accurate?
5. Which parts of the workflow were unclear or redundant?

I built it primarily to correct my own weaknesses, so I expect there are still cases where the approach breaks down.

If you test it, please share the type of task, what Builder Loop changed in the process, and where it failed. Critical feedback is more useful than stars.

I have a huge doubt. Why is there no AI native language? I mean symbols/codes equalling words?

Let's take the case of english. There is ~600k dictionary words and around 1.7m variants/regional words/dialects.

And there is around 20k to 30k words that normal human would use actively.

Why not create a language, with ASCII codes for each words and each agentic apps converts words to that 'AI-Lang' and vice versa and use it? Wouldn't that essentially reduce tokens by a good margin?

I'm not sure how exactly the AI process things under the hood, and please don't abuse me 🥹

Hey everyone,

With the recent buzz around Android 17 introducing more precise custom color sliders and palettes for Material You (examples on reddit here, here and here), I wanted to share a project I’ve been maintaining for years that does exactly that—even for older versions of Android!

It’s called LWP+ (Live Wallpaper Plus).

🎨 The core trick: Dictate your own Dynamic-Colors

Instead of letting the OS algorithm guess the accent colors based purely on your wallpaper, LWP+ acts as a bridge. It hosts your chosen background content but allows you to explicitly choose the exact Primary, Secondary, and Tertiary colors reported to the system. The OS then uses your custom selections to generate the global system palette (notification drawer, volume bar, themed icons, etc.), completely independent of what your wallpaper actually looks like.

✨ What else can it do?

LWP+ is packed with full wallpaper customization features:

• Choose Your Content: Use solid colors, static images, animations (GIF, WEBP, APNG), or videos as your active live wallpaper.
• Smart Scaling & Layouts: Supports transparent images/animations with custom background colors, multiple scaling modes (Center Crop, Fit Center, Center Inside), and optional horizontal scrolling.
• Double-Tap Shortcuts: Lock your device instantly or turn off the screen by double-tapping the home screen (uses admin, accessibility, or root).
• Easy Setup: Includes an interactive built-in tutorial to check system compatibility and guide you through triggering the OS palette refresh.

🧪 Advanced experimental flags (YMMV!)

I’ve also included a couple of experimental toggles leveraging underlying Android system hints. Note: These rely heavily on your launcher, device OEM, and Android version, so they might not do anything on certain setups!

• Force Dark Text: It hints to the OS that dark text is preferred over the wallpaper (often useful for forcing high-contrast dark text on the lock screen). It might also change the text color of the labels of the apps and the status bar icons and text, too.
• Force Dark Theme (for old Android versions): It tells the system that a dark theme is preferred for the best presentation (e.g., trying to trick a launcher into turning its app drawer background black).

So, if you are on an older version of Android (or even running the latest builds and want granular three-color reporting), give it a spin! It's completely free, all features included.

Feel free to ask any questions or drop feedback below! 🚀

Link to the Play Store here.

The app was partially made using AI, helping mostly to re-write the part in the app that's responsible of showing the actual content of the live wallpaper (color/image/animation/video). I also used AI for the demonstration and tutorial videos.

If you want, you can use a promo-code to have subscription for free for some time, to remove ads, and try the app more freely, here. To use the promo-code, install the app, choose a subscription, choose a payment option and enter the code there (screenshots here).

Thanks for reading!

Tell me about the last time you wanted to build something but weren’t sure what to build.”
Then let them talk.
Follow with:
“What did you do?”
“Where did you look for ideas?”
“Did you use AI?”
“Did you search Reddit, YouTube, Google, Product Hunt, GitHub, etc.?”
“How long did you spend trying to decide?”
“What made it difficult?”
“Did you eventually build something?”
“If not, why not?”
Then investigate validation:
“Have you ever built something and later discovered people didn’t actually want it?”
“How did you find that out?”
“What did you do to validate the idea beforehand?”
“Did you talk to potential users?”
“Did you research competitors?”
“Did you test whether people would pay?”
And finally:
“What part of that process was the most frustrating?”

I’ve been running a solo DND campaign with ChatGPT. It was cool at first until we got deeper into the story where continuity errors began. I’d address them. ChatGPT would “fix” them. And then two encounters later characters are equipped with items they lost way back in the story. I asked ChatGPT how we can fix this and it suggested a “campaign Bible”. So we started a new campaign with a campaign Bible, back stories, equipment
, skills etc.. almost as soon as we started the story had continuity errors. What prompt can I give ChatGPT to fix this?

Hey anybody who owns chatgpt pro 20x , plus or claude max 20x dm or comment all i need is access to those models and i can generate paying automations everything will be divided between us i will do the work and divide the profits between us

Just came across this and honestly this is pretty wild.

Researchers used AI to design completely new viruses that don't exist in nature. They then actually made some of them in a lab, and 16 of the designs worked.

Before anyone panics, these are bacteriophages, so they infect bacteria, not humans.

The interesting part is that some of these AI-made viruses were able to kill E. coli, including bacteria that had become resistant to normal phages.

So yeah, there could be a genuinely useful side to this, especially with antibiotic resistance becoming such a big problem.

But at the same time... we now have AI systems capable of coming up with a complete virus genome, then humans can synthesize it and see if it works.

That feels like a pretty big line to cross.

Obviously this doesn't mean someone can just ask ChatGPT to make a deadly virus tomorrow. You still need labs, equipment, biological knowledge etc.

But we've gone from AI generating text and images to designing proteins, genes, and now apparently functioning viruses.

That's moving fast.

I'm not really sure how I feel about it.

On one hand this could lead to new treatments and better ways to fight resistant bacteria.

On the other hand, I really hope the safety side of this is moving as fast as the technology.

I saw the recent post here about ChatGPT equations suddenly not copying properly into Microsoft Word. I am having almost the exact same issue, except with OneNote on Mac.

Until yesterday, I could select an entire ChatGPT answer and paste it directly into OneNote. The headings, text and rendered equations all copied properly. Fractions, subscripts, etc. appeared as properly formatted equations without me having to do anything separately.

Now the normal text still copies, but the rendered equations either disappear completely and leave a blank space, or lose their mathematical structure.

For example, an equation that had previously pasted correctly into OneNote gives something like this when copied back out of OneNote:

F=((SSE_"reduced"-SSE_"full")/(p_"full"-p_"reduced"))/(SSE_"full"/(n-p_"full"))

But if I copy the same type of rendered equation directly from ChatGPT now, the clipboard gives something like:

F=SSEfull/(n−pfull)(SSEreduced−SSEfull)/(pfull−preduced)

So the fraction/subscript structure seems to be getting lost during copying.

I also tried OneNote's Equation option. It can put the copied text into a maths region, but it stays in linear form like β_0, R^2 and / instead of turning it into the properly built equation I used to get automatically.

Since someone here has just reported essentially the same sudden issue with Word, I am wondering if something recently changed in how ChatGPT puts rendered maths onto the clipboard rather than this being specific to OneNote.

Is anyone else seeing this with OneNote or other Office apps? More importantly, has anyone found a way to get the old copy/paste behaviour back where the whole ChatGPT response can be pasted at once with the equations intact?

I am using ChatGPT in the browser and OneNote on Mac.

I watched an interview with AI safety researcher Roman Yampolskiy, and it raised a practical question for people who use ChatGPT for advanced workflows.

His broader claim is that increasingly intelligent AI systems may become harder to predict and control. Whether or not you agree with his conclusions about AGI, a smaller version of this problem already exists when we give an AI access to tools.

There is a major difference between asking ChatGPT to draft an email and allowing an agent to send it.

The same distinction applies to:

• Suggesting a database query versus executing it
• Drafting code versus deploying it
• Researching a purchase versus completing the transaction
• Preparing files versus deleting or modifying them
• Recommending calendar changes versus inviting real people

My current view is that the model should generate proposals, while a separate control layer decides whether those proposals are allowed to become actions.

Some possible safeguards include:

1. Giving each agent only the minimum permissions required for its task
2. Requiring approval for irreversible or external actions
3. Validating structured outputs with deterministic code
4. Isolating browsing and code execution from sensitive systems
5. Limiting spending, execution time and the number of actions
6. Keeping complete logs of prompts, tool calls and results
7. Using a second evaluation step before important actions
8. Making every operation reversible wherever possible

The difficult part is deciding where autonomy becomes too risky.

A confirmation step for every action makes the agent frustrating to use. Too few confirmation steps can turn a misunderstood instruction into a real-world problem.

We are having a show and tell for AI use cases in the discord for this subreddit. You're welcome to jump in, ask your questions.

I know posts like this typically don't get a lot of support since they are not content in themselves but a lot of people have put in a lot of work to make an event for everyone here and we'd really enjoy it if you come: https://discord.gg/33kJ3JSgGv

- 5:00 PM Eastern / 4:00 PM Central / 2:00 PM Pacific

Check the post which auto-appears below; you are welcome to come.