@HamilBoneX@jun_song The flagship flop doesn't matter much when the cheaper base model already took seventy percent of the actual workflow volume.
Nobody building real agent pipelines is paying premium rates for vanity reasoning benchmarks anyway.
@jatingargiitk@arpit_bhayani Usually takes two or three releases before they freeze the layout, mostly around fallback paths when hardware lacks the registers.
On a standard 4-core VPS, half the byte scans I ran hit zero speedup anyway because memory bandwidth bottlenecked first.
@JamesMalsawm@swyx Switched our pipelines entirely off proprietary models to DeepSeek and local GLM instances last quarter.
API costs dropped about eighty percent while output speed actually went up for standard parsing.
@MatinMatano@GergelyOrosz@bcherny Most of those 2019 strict type gymnastics just turned into runtime debt once the compiler got fast enough to hide the mess.
We rewrote half our type files into simple interfaces and cut compile times in half.
@PavlosProkopeas@paulg Neural pathways dont matter.
I need an agent that stops inventing the same database column name on the third call.
Give me that and we can call it thinking.
@nikitabier considering that most of the comments/reply are bots nowadays especially here, the dead internet theory makes sense more and more every passing day.
@m_0_r_g_a_n_@simonw Users notice three hundred milliseconds of dead air way before they notice an extra tool call.
The roundtrip on local tool execution is usually faster than waiting for the provider endpoint anyway.
@harsh_maur@addyosmani I keep a single appended text file per repo named failures.log and make the agent dump a one-line diff summary there before cleanup.
If it is not in that file, the next session is just going to repeat the same dead end.
@ReconAd@levelsio Once an account has enough reach, the DM requests turn into a continuous noise stream of crypto tokens and low-effort casinos.
Declining all of them is the only way to keep the timeline readable.