I think the reason is that after all the benchmark comparisons, anthropic models still perform much more reliably and better in day to day use. Every day now there is a new supposedly fable level model that then somehow completely vanishes in a couple of days. While fable is still the best model and if only it were cheaper less people would be hating I suppose.
This really reminds of the spec comparison obsession android users had in the 2010s. Every 6months an 'iPhone killer' was released, but they were only ever superior in total isolation.
if you're using herdr, i built a plugin exactly for this reason. its a pwa so requires a proper frontdoor for secured access (tailscale, netbird etc.).
Using it now. It seems like I finally found something that works. Having the same agents accessible on the laptop in a large TUI and quickly viewable from my phone has been useful.
Tailscale integration works well too. The mobile notification setup was a little clunky and the instructions were confusing, until I found the directory with the shell scripts. It should probably expose a herdr action to generate the keys and include that in the documentation.
One thing though: the subagent lines should be clickable like they are in Termux but your actions box absorbs the click and focuses the GUI input line.
I've been working on collie[1] a plugin for herdr that runs a web app on the herdr host which can then be served via tailscale for example. This has made accessing my agents on the go a lot easier, paired with session persistence from herdr it's been quite nice to work with.
They should opt out of Google. What they are doing here in open daylight is insane. They stole the entire internet to train their models and are now shoving AI overviews down everyones throat. Clicks to publishers were already decreasing over the last years even without AI, but 2026 is showing what the future will look like, zero clicks from Google, because they dont care. Google got what it wanted and is now racing to AGI spending all its cash on datacenters, not caring about all the casualties along the way. Google, on its current trajectory, is truly evil in my opinion.
I'm really not a fan of regulation, but someone has to step in and have Google seperate its products, search should be free of AI overviews imo.
I'm with you in spirit but publishers are hardly blameless. SEO optimization and framework bloat made internet browsing downright unpleasant since the late 2000s.
Google is delivering a solution to a problem they [indirectly?] created.
So I guess we (and a lot of other people) have had the same problem, which is managing your agents on the go. I decided to build a plugin[1] for my terminal multiplexer (herdr) to access the sessions via PWA served through a tailnet.
check out tsnet if you are building on tailscale anyways. your app can then be a tailnet node that can read tailnet WhoIs data and have access controlled via tailnet ACL
"Flash-Lite" is a different product from "Flash", which is more expensive. They couldn't be more confusing with their naming though, especially since they have 3.1 Pro and not 3.1 Flash non-lite.
It depends on the use-case. yes, 90% of cost is cache in agentic coding scenarios (actually 95% in my experience). But not when the model reasons for 200k+ tokens before answering a complex problem.
$0.15 / million tokens
$1.00 / 1,000,000 tokens per hour (storage price)
I much prefer the OpenAI/DeepSeek way of pricing caching where you don't have to think about storage price at all - you pay for cached tokens if you reuse the same prefix within a (loosely defined) time period.
I confirmed this by running a bunch of prompts through Gemini 3.5 Flash without doing anything special to configure caching and noting that it comes back with a "cachedContentTokenCount" on many of the responses.
In our experience, caching is not very reliable with google. We always get random cache misses that don't happen with other providers. We find OpenAI, Anthropic and Fireworks (which we use a lot) all have higher cache hit rates. So it's not only about the costs of cached token but also what kind of cached hit rate you get.
In my experience Google is the most flaky in general, which is surprising considering the rock solid history of their search and other products. Just more likely not to respond at all, to give a response out of left field, to handle the same error in 12 different ways randomly (a rainbow of HTTP status codes and error messages), etc etc.
I agree. The https://aistudio.google.com/ is shockingly bad. I'm not sure I've ever used such a flaky Google service before. It's so much worse than Gmail or Google, not to mention ChatGPT or Claude or DeepSeek or Kimi or Midjourney web interfaces. The bizarre janky integration with your Google Drive, or Gemini or NBPs randomly erroring out, often indefinitely. I've had sessions refresh themselves and just... disappearing. Or when you get frustrated with a buggy dead session and hit 'new session' and have to wait minutes for 'saving...' to happen.
Exactly our experience too. Effectively we catch these and on these status codes, we send to OpenAI. Retrying the same query in Gemini has high chance to give kind-of the same status code.
yah, which means that the input cost is the only value that should be paid attention to at the end + the cache discount (x10). If google would start offering x20 discount it would make it twice as cheap while input and output stayed the same.
