There’s usually a very real and very hard to describe data related impracticality that voids the usefulness of a design that appears well thought out and complete.

Additionally enterprise AI products are built on custom integrations, and complexity of maintenance overwhelms the engineering team and leaves very little time to build out new things.

The simplest changes that come from knowing insider customer experience have significant impacts. If the default range for a duration filter is 5-30min, and it turns out the most interesting data is really on 1.5hr+ rows. Or adding search across legacy platforms that bury uniform information under deeply nested modals, which people spend 20+ a week clicking through to collect a usable sample set based on existence of a few keywords. But building a system that returns good search results is the hard part.

I do like the “build on top” pieces in your gallery. If it’s fast and reliable enough to collab during a discovery meeting, or a customer success meeting, that would be genius. Because then you’d have a way to pull customers into the right mindset to articulate frustrations with their current software, iterate on getting those frustrations get translated into concrete designs together, and at the end you walk away with something that proves you both understand and can solve their problem to any audience.

Funny you mention the Ford SuperVan because that’s much closer to the 919 Evo in the "no homologation no limits" category than anything you could register and drive off a lot. A fairer and much more impressive benchmark is the road-legal Ford Mustang GTD running a 6:52. That's still far quicker than the BYD, with roughly two thousand less horsepower.

There's major road closures for key arteries like Market st, Embarcadero, fisherman’s wharf, and the Presidio. Traffic always crawls and downtown will become a maze. Even 'human' drivers struggle because you can't cross large boundaries of the city.

Waymo launched in the city about a month before last year’s race. I took one to the starting line, but it couldn’t reach the actual drop-off. It stalled about 0.3 miles away and I had to run the rest. The issue wasn’t the route, but the chaos. Dense foot traffic, impromptu street closure re-routes, and unpredictable crowd behavior were hard to autonomously solve.

Tesla's robotaxi launch will have to overcome the same challenging mix of realtime conditions: limited access to closure data, learning of impromptu re-routing logic, unpredictable human crowds.

Definitely it’s a bold move to launch this weekend. If it works, great PR.

"What's the tone of this conversation?"

I consistently get this response:

"Please re-upload the image as a supported format like .jpg, .png, or .webp. The current file type can't be viewed, so I can't assess the tone yet."

However, when I use the exact same image and instead ask:

"How are people feeling in this conversation?"

it responds correctly with an emotional analysis of the screenshot.

Even stranger one is when I type something like:

"I see the tone of the response has changed"

the app just shows the black flashing square (as if it's thinking), but never returns a response.

It seems like the word "tone" might be breaking something in the input parsing or triggering a silent failure. Has anyone else run into this? Could this be a prompt classification edge case or a bug in the image pipeline?

Comments URL: https://news.ycombinator.com/item?id=44232288

Points: 2

# Comments: 0

Code’s here: https://github.com/devin-liu/excel-to-markdown

Single-cell RNA sequencing of APCs then identified a new committed preadipocyte population that is age enriched (CP-A), both in mice and humans. CP-As displayed high proliferation and differentiation capacities, both in vitro and in vivo.

It should read more like "aging triggers emergence of a specific stem cell type (APCs) which drives visceral fat expansion"

We once interviewed a former national [racket sport] champion from [country] for a React/JS SWE role. Our in-house [racket sport] expert, who happens to be the best player among us, was sure they’d ace the coding exercise. The next day, when we asked for his verdict, he gave us a few words: “He sucks. He got absolutely nowhere.”

Lesson learned: extreme talent in one domain not always a predictor of aptitude in another.

That kind of thinking lacks vision and makes it really hard to build anything meaningful. Over time, it wears you down. Even if the product “works,” it doesn’t feel good because you know you're forcing it. These folks tend to say things like “this should be easy,” then only show up to question why something isn’t done. They manufacture urgency instead of clarity, and when things inevitably fall short, the blame falls on the technical team.

Second, you’re not a first-time founder anymore. Life’s short. For me at least, you’ve earned the right to walk away and build something you actually care about again.

For me, Roger Federer's style represents tennis at its most beautiful. His all-court game feels effortless and graceful, almost like a dance. But from a court-level view, it's more of a high-speed chess match built on calculated aggression, constantly pressuring opponents and waiting for the slightest opening to strike a point-winning shot. That level of sophistication and precision wouldn’t be possible without modern racket technology.

I still feel emotionally tied to classic matches from my childhood, especially Federer versus Nadal. But there's no objective reason, because tennis keeps getting better. People worried finesse was disappearing, but players like Alcaraz have brought back drop shots and clever cat-and-mouse tactics against deep-baseline defenders like Zverev and Medvedev. It’s a technique that was once considered too risky to rely on consistently.

In golf, tennis, baseball, basketball, running, & any other sport will keep evolving as technology & athleticism improves. Clinging to older styles feels more like holding onto the past than genuinely appreciating progress. If you can’t enjoy Curry hitting daggers in the Olympic finals or Kiplimo breaking 57 minutes in a half marathon, maybe the problem isn't with the sport itself. Maybe it’s the comfort of past memories holding you back from appreciating what’s happening now.

- Breaks tasks into clear, actionable RPG-style quests with XP rewards

- Uses game mechanics like levels, XP, and achievements to keep you motivated

- Offers a distraction-free UI with dark mode

- Sorts tasks by complexity (Intern to Unicorn Status)

Bring your own OpenRouter key.

Check out the code at: https://github.com/devin-liu/taskventure

Watch the demo video in the readme: https://github.com/devin-liu/taskventure/blob/main/README.md...

Comments URL: https://news.ycombinator.com/item?id=43019177

Points: 2

# Comments: 0

That’s why it becomes such an issue when customers come in requesting an alteration—it’s like being dropped into a team at the final stages of a project that leadership says is 90% done, but it’s been stuck for weeks trying to finalize that last 10% due to some "small last minute requirement changes"

I first started tracking my runs with apple health, basically carrying my phone in my pocket to measure distance. Back then, I had no weekly mileage targets, or pace goals. Just a curiosity about how far I could run. Eventually, I switched to Strava. I felt a bit of friction around starting and stopping runs on the app, but I loved watching my paces gradually improve month by month.

Eventually I signed up for my first marathon, taking my iPhone in my pocket and first gen airpods that ran out of battery halfway through, but I finished in 3:48. I stuck with the iPhone for a while, but one day I zoomed into the strava map and realized the iPhone’s GPS was unreliable—it added zigzags to my routes, inflating my mileage and making me seem faster than I really was (massive ego bruise). So I went to research accurate GPS watches, and I remember seeing people test them by running straight lines to check for accuracy on a map. The forerunner was the most satisfying straight on the map, and so I bought that in May 2020.

So I’ve had a garmin since May 2020 and still love it. The simple start/stop mechanism has become a ritual for me. I also appreciate the heart rate screen, which shows my zone using colored ranges—it’s what I used to pace myself during races. For example, I’d aim to stay under 160 bpm during half marathons and marathons. With the Forerunner, I brought my time down to 3:11 for the marathon and 1:24 for the half marathon. That’s when I hit an inflection point: I couldn’t improve further without serious training plans.

I tried using Garmin Coach but made the mistake of choosing plans slightly below my fitness level. As a result, I didn’t run enough hard workouts and plateaued. After that, I lost motivation and took a break from running and lost fitness-- my old 130BPM pace became my new 160BPM pace. When I returned, I spent a year trying to regain it. I watched countless YouTube videos and read Reddit threads claiming, "every amateur runs too fast and too few miles." So I focused on high mileage without prioritizing aerobic envelope workouts. My fitness stagnated—my half marathon slowed to 1:27, and my 5K and 10K times didn’t improve. I also psyched myself by overshooting mileage targets, leaving me either sick or over-fatigued on race days.

Eventually, I gave myself permission to run hard again, and my fitness returned. I worked my way back to a 3:02 marathon last year. Now my favorite workflow involves using the VDOT app as my personal coach. I set a weekly mileage target, specify which days I can handle hard workouts, and it generates a detailed plan for me. For example: warm up for 2 miles, run 400m at a target pace of 5:40 with 1-minute rests, and cool down for 2 miles. The garmin integrates as what I call my "buzz coach" through each stage of the workout. Too fast? Buzz. Too slow? Buzz. Next lap? Buzz. The alerts really help with making real-time adjustments. Overall I find this setup eliminates the decision fatigue of training. I used to obsess over pacing, distance goals, and analyzing every bit of my data. Now it feels like I'm just getting outside, running a lot, and having fun with it—and ironically, I've just started improving again.

In my opinion, this AI development stalemate is more layered. Big companies set such broad targets in a race to catch up with OpenAI that they lose focus on real use cases. So the loudest voices, those good at navigating internal politics, end up in a good spot to push their own ambition over actual customer needs or technical practicality. They set goals that sound just a bit more exciting than their peers, which pulls resources their way. But the focus shifts to chasing KPI's rather than drilling into real problems. Even when they know going smaller is smarter, knowing and doing are two different things.

It’s still a great time for small AI startups. My favorite kind is a team that quickly learn a business’s needs, and iterate toward the right interaction points to help. I think just staying focused on solving a lot of small related problems very fast, you can create something that feels like a real solution.