101–110 of 1001 posts
With each release from the the other major labs, it becomes harder for Google to tell a compelling story about Gemini 3.5. Edit: Gemini 3.5 Pro . Expectations grow with each day it is not released.
Is there a reason the AI companies usually announce new products so close to each other. Like not just the same day but literally hours apart. GPT Live then an hour later Grok 4.5. As if they try to one up. I expect something new from Anhtropic as well today.
Maybe it‘s the Nash equilibrium from a timing perspective? Like the reason that close to a McDonals there is usually a Burger King.
With each release from the the other major labs, it becomes harder for Google to tell a compelling story about Gemini 3.5. Edit: Gemini 3.5 Pro . Expectations grow with each day it is not released.
Earlier quoted context omitted.
My gut feel is Anthropic is very technical and pedantic which makes their models really technical and pedantic. They're top at code and technical benchmarks but anecdotally I've found OpenAI to be significantly farther ahead for general usage. Opus 4.8 will burn 10k tokens trying to answer something 100% whereas GPT-5.5 will burn 2k getting it 90% which is good enough for many things. Some personal testing on a "help…
The problem is that the remaining 10% can bite you in bad ways. I was in Cotswolds, UK a couple of months ago. For those of you who don't know, it's a rural region known for its "chocolate-box" villages and honey-colored limestone architecture. Basically, you go from village to village, most commonly via bus, taking in the sights and doing touristy stuff. When planning the trip, my sister used ChatGPT, which helpfull…
If the central question was "what is the bus schedule on `day`" and the model screws that up, it gets a fail in my book.
Also curious if Google Maps gets the timetables correct (assuming it has them).
Semi-related, I also discovered that the default web search/fetch tools are pretty primitive and Exa MCP annihilates them. I ended up doing some comparisons with Claude Code comparing built-in server-side to Exa and to a Python MCP that used SearXNG for search and Exa was a clear winner and Python+SearXNG ended up coming out roughly the same after a few cycles of letting Claude optimize the Python code and adjust SearXNG settings. Ultimately it landed on this (making some changes to optimize returning relevant context directly in the search results so the model didn't need an additional web fetch call) https://gist.github.com/nijave/604c43e3e0fdcd60f5280d3a6b109...
It seems to be extremely economical - 4x better reasoning efficiency compared to Opus while being priced at $2/$6. For comparison, GPT 5.4 is $2.5/$15, GPT 5.5/5.6 are $5/$30, Opus 4.8 is $5/$25, Fable is $10/$50. And by benchmarks (unless they gamed them), seems to be at around Opus 4.7 level, which is what Elon mentioned in https://x.com/elonmusk/status/2074911038286295049 . I guess the Cursor data was very useful.
With each release from the the other major labs, it becomes harder for Google to tell a compelling story about Gemini 3.5. Edit: Gemini 3.5 Pro . Expectations grow with each day it is not released.
Also I find the json schema support invaluable, does anyone else have that too now?