The most recognizable failure in agent-assisted coding: the agent tries a fix, hits an error, apologizes with real conviction, and produces the same fix with the variable names shuffled. The apology is what makes it maddening. It saw the failure. It acknowledged the failure. Attempt five is already running.
The explanation isn't that the model is dumb. It's that the model is stateless: every turn, it re-reads the entire session transcript, and that transcript is the whole world it can see. Count what's in that world after four failed attempts. The dominant pattern, by sheer volume, is the failed attempt itself. A human reads that history as mounting evidence to change course. A next-token predictor reads it as what this session does. The history doesn't fence off the bad path it paves it. Even the apology deepens the groove, because apologize-then-retry is a pattern it has seen thousands of times and is faithfully continuing.
There's research that caught this on camera: in one trajectory study of coding agents, failed runs had located the correct file 72 to 81 percent of the time. Finding the right place was never the problem. Letting go of the hypothesis was.
The practical fixes live outside the prompt. A hard budget on turns or tokens, so a stuck run stops instead of politely burning money. A repetition tripwire: if a new attempt looks like the last one, force the agent to list hypotheses it hasn't tested, so the untried paths exist in the transcript and can compete. And the one the vendor manuals themselves recommend: clear the window. The fresh session isn't smarter. It just starts without the gravity, and the constraints you learned travel forward weighing a few dozen tokens instead of four failed attempts.
I've noticed a problem with the way I discover and use digital tools. Whenever I find a useful website or app, I try it out, but after some time, I often forget its name or where I found it.
I've tried saving links in bookmarks, but eventually, they become difficult to organize, and I rarely go back to check them.
I'm wondering if this is something other people experience too. It feels like discovering a useful tool is only half the challenge; remembering it and actually using it later is another.
Hey guys i want to know, at what stage do you start running paid ads? By this I mean facebook and google ads, i dont have a mobile app so not on the app store. And what tools do you use to monitor analytics? I know most of us use stripe, but after some feedback there to help. Appreciate your support
Yesterday, someone posted on how they have nailed work through VAs on Reddit, and I've been curious ever since. Reddit is one channel I've spent hours on every day but never efficiently marketed. Check @nathan_may1's comment here: https://www.producthunt.com/disc...
Let s say you re curious about starting a new company/project, how do you spot any rising trends to work on? It can be Reddit, Google Trends, Instagram - whatever works for you.
!! The article is about the launch listing platform, not the SEO tool.
StartupBase has a feature called Outrank. Paste a URL, pay $15 or more, and your rank on the leaderboard is the total youve ever paid. Top 8 hold the homepage, and this has gone mini-viral last week.
Credit where due, the copy is the most honest ive read on a launch site: "pay what you want, earn a DR 72 dofollow backlink."
One thing I think engineers learn later than they should: Being technically strong is not the same as being valuable to the business. You can build APIs, design databases, improve architecture, and write clean code. But eventually someone has to connect that work to: - revenue,
- cost,
- risk,
- speed,
- reliability,
- or customer experience. I ve been thinking more about this lately because writing the code is becoming less of the differentiator. The harder skill is understanding why the thing should be built, what tradeoff you re making, and what outcome the business actually cares about. For engineers here: What helped you get better at connecting technical decisions to business impact?
