Comments URL: https://news.ycombinator.com/item?id=47450049

Points: 2

# Comments: 3

Comments URL: https://news.ycombinator.com/item?id=47173710

Points: 20

# Comments: 1

Pretty close to Gemini 3 Pro Image (aka Nano Banana Pro) in most benchmarks, even without thinking+search, and even exceeding it in 2 most important ones of 'Overall Preference' and 'Visual Quality'. I'm excited about the big jump in Infographics/Factuality (even without thinking+search; I'm surprised that text+image search grounding doesn't make an even bigger dent).

Comments URL: https://news.ycombinator.com/item?id=47139651

Points: 1

# Comments: 0

Could be useful for planning too, given its tendency to think big picture first. Even if it's just an additional subagent to double-check with an "off the top off your head" or "don't think, share first thought" type of question. More generally would like to see how sequencing autoregressive thinking with diffusion over multiple steps might help with better overall thinking.

Apart from that, the usual predictable gains in coding. Still is a great sweet-spot for performance, speed and cost. Need to hack Claude Code to use their agentic logic+prompts but use Gemini models.

I wish Google also updated Flash-lite to 3.0+, would like to use that for the Explore subagent (which Claude Code uses Haiku for). These subagents seem to be Claude Code's strength over Gemini CLI, which still has them only in experimental mode and doesn't have read-only ones like Explore.

Token costs aside, arguably fresh context is also better at problem solving. When it was just me coding by hand, I didn't save all my intermediate thinking work anywhere: instead thinking afresh when a similar problem came up later helped in coming up with better solutions. I did occasionally save my thinking in design docs, but the equivalent to that is CLAUDE.md and similar human-reviewed markdown saved at explicit -umm- checkpoints.

Quite a premium for speed. Especially when Gemini 3 Pro is 1.8x the tokens/sec speed (of regular-speed Opus 4.6) at 0.45x the price [2]. Though it's worse at coding, and Gemini CLI doesn't have the agentic strength of Claude Code, yet.

[1] - https://x.com/claudeai/status/2020207322124132504 [2] - https://artificialanalysis.ai/leaderboards/models

Wifi obviously has higher bandwidth, but I guess it isn't viable as a mesh, or is there any trick with turning on/off hotspots on phones dynamically that'd make it viable? (Afaik older phones made you pick between being a hotspot or being a regular wifi client, but at least some newer ones seem to allow both simultaneously.)

I'm definitely hoping for a future with wider support for C2PA (content credentials on images) on phone cameras to make these photos power citizen journalism. So far Samsung S25 and Pixel 10 support C2PA in the camera hardware: need other phone makers (especially Apple) to get on board already... if you're an iPhone user, please help yell at Apple support etc!

Aside: I registered a domain and plan to build a citizen journalism news feed for such photos (and uncut videos). I see it as the antidote to Instagram et al's feeds that're full of AI slop (and plenty of fakery even before AI-generated imagery got big). And it's essential to truth, democracy and ultimately (maybe I'm too idealistic here) peace. Aside to the aside: wish some of us techies banded together to build "peace tech" as a new sector in tech, DM if interested in brainstorming or working together.

But then how will we review each PR enough to have confidence in it?

How will we understand the overall codebase too after it gets much bigger?

Are there any better tools here other than just asking LLMs to summarize code, or flag risky code... any good "code reader" tools (like code editors but focused on this reading task)?

How are y'all reviewing all this massive code output? How can it possibly scale, as the agents run faster or as you add agents?

I guess we'll have to learn to give up some control; we'll stop reviewing all lines of code, and increasingly rely on AI tools to summarize and flag specific lines. Are any tools good at this?

More generally, how do you build confidence in the code, both at the PR level and eventually at the codebase level (when 90+% of code in it will be written by agents)?

Am I too worried about code review, are there bigger bottlenecks in our jobs when using these coding agents?

Comments URL: https://news.ycombinator.com/item?id=46557580

Points: 2

# Comments: 1

General cautionary tale: just coz a company is successful, doesn't mean it's doing _everything_ right. Plenty of folks who love their Teslas would prefer a few more buttons (and door handles on the inside, etc) if given the choice. Could say similar things about some choices Apple made.

I'd go further: what's valuable is code review. So review the AI agent's code yourself first, ensuring not only that it's proven to work, but also that it's good quality (across various dimensions but most importantly in maintainability in future). If you're already overwhelmed by that thousand-line patch, try to create a hundred-line patch that accomplishes the same task.

I expect code review tools to also rapidly change, as lines of code written per person dramatically increase. Any good new tools already?

Coursera's model will still survive for a while, given people's desire for branded credentials (university degree credits or company-branded certificates)... until the university bubble bursts too in a 10+ years. Start of trend: https://www.nbcnews.com/politics/politics-news/poll-dramatic...

A bit of a plug: we tried building a consumer business, with a learning experience built atop these LLMs: https://uphop.ai/learn . Still offered for free to consumers, but we're now succeeding much better on B2B ("you either die a consumer business or live long enough to become B2B" was v true for us).

Comments URL: https://news.ycombinator.com/item?id=46296338

Points: 2

# Comments: 0