The i-have-adhd skill going around that stops coding agents from burying the answer: lead with the action, number the steps, no "Great question", no "Hope this helps". Good rules. We went and read why the model buries the answer in the first place, and the answer is two different habits that deserve two different treatments.

The first habit is padding, and it is free to remove.

It turns out, reward gains are largely driven by longer responses, and both major LLM leaderboards had to add length control because their judges preferred longer answers. The openers, recaps and closers are tokens a rater once liked. Nothing downstream depends on them.

The second habit is the reasoning paragraph, and it is only free to remove when the model has somewhere else to think. A language model writes one token at a time, so if it reasons in text, the reasoning has to come before the conclusion. Tests show what happens when you flip that order: reasoning written after the answer scored the same as no reasoning at all, while reasoning first more than doubled the score. Concise reasoning kept the value; absent reasoning kept none. With hidden thinking turned on, the visible reply can lead with the answer and the cost moves to hidden tokens. With it off, an answer-first rule asks for the verdict before the computation.

Makers,

Good morning.

For my work, I need to connect to multiple LLMS and APIs - the more the merrier. I don't want to go and register for all of them + to pay monthly fee, if I am going to use them very rarely.

Two people in one Reddit thread called a comment of mine AI generated, in the same register I have used across every platform for weeks without issue. Went back and audited the account instead of arguing about it.

The pattern that got flagged was not length or structure. It was confident mechanism reasoning with no first-hand marker in it, nothing that said I actually did this. Comment karma on that account went from 1 to 26 over two weeks once every comment started carrying a real number or a real mistake instead of just an argument.

Curious whether anyone else here has found a similar tell for what reads as generated versus what reads as lived-in, especially posting into communities that skew technical.

Google shipped Gemini 3.8 Flash this week and every post about it so far is coding benchmarks and "look what it built in one prompt" demos. Which is fine, but I don't write code for a living, so it's not telling me much.

Genuinely curious if anyone's actually thrown real work at it yet, not a toy prompt. I keep seeing "our best model yet" every few weeks now and I'm starting to tune it out a little, which probably isn't a great instinct given how fast this stuff moves. Anyone else feel that way, or am I just being lazy about keeping up?

I spent nearly four years on one idea before I finally pivoted to what I'm building now (@Basedash: AI data analyst). What mostly bothers me isn't that I was wrong, it's that I still can't tell you the exact signal that should've made me quit two years earlier.

At the time everything felt like progress. We had users, we had encouraging conversations, we had the occasional good week that convinced me the next one would be better. None of it was a clear "stop."

We are building our pre-launch presence the way everyone says to: answer questions, be useful, do not pitch. Reddit seemed to be going fine. Then I checked properly and found that every comment we had left in one subreddit had been removed within seconds of posting.

The part worth knowing is that we could not see it. A removed comment still looks completely normal to the person who wrote it. It sits there on the thread when you visit your own profile. The only signal is a bot reply, and that lands under Notifications rather than your inbox, so if you are not looking for it you will not find it. We lost two days before noticing.

The mechanism is something called Contributor Quality Score. It is a site-wide score Reddit assigns your account, separate from karma, and subreddits can set automod to reject anyone below a threshold. You are not shown your score anywhere in the interface.

There is a way to check it. A subreddit called r/phcareers runs a public thread where you comment and a bot replies with your band: lowest, low, moderate, high or highest.

Last week I saw the Neo Gama model on Product Hunt humanoid for house chores.

Today, it is Protoclone and there are even more.

One model is pricy now, but a one-time investment may be cheaper long-term than ongoing salaries from the point of view of a company.

As Makers, we're constantly juggling product builds, user feedback, marketing, and bug fixes. Time seems to vanish in an instant. Over the past year, I ve found that sometimes it's not a grand strategy or overhaul that makes the difference, but rather one game-changing tool that quietly handles the busywork.

For me, a standout has been Fathom, an AI-powered tool that transcribes meetings and automatically delivers highlight summaries. No more scrambling for bullets during calls, no more post-call backlog. It s saved me hours each week. In fact, users combining tools like Fathom, ChatGPT, Asana, and Zapier have reported reclaiming 20+ hours per week by streamlining writing, research, and task automation.

Genuine question for anyone shipping MCP servers or agents.

The model picks a tool that isn't right for the request. Or it picks the right one and fills a parameter with something you never anticipated. What's your actual process for fixing that?