kache
@yacineMTB ·
There is no amount of money you could pay me to be a DevOps engineer for a crypto exchange
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Daniel San@dani_avila7 ·
The IDE is not dead… but the way we build software is changing
Today I gave a talk at Latam Build: “The Rise of the ADE” (Agentic Development Environment)
I shared how I’m moving from writing code to orchestrating agents, and what six weeks of working without an IDE taught me
Here are all 23 slides 🧵
roon@tszzl ·
rip king
dax@thdxr ·
so did all those people buying mac minis for openclaw not use them long enough to hit this
swyx@swyx ·
excited for @Cloudflare's debut as a frontier lab
#NothingButNET gang, assemble!
- kache
@yacineMTB ·
- kache
@yacineMTB ·
Artificial Analysis@ArtificialAnlys ·
Utopai X (based on MiniMax H3) takes #2 on AA-Video-I2V v2.1, just behind Dreamina Seedance 2.5
Utopai X is the video model from Utopai Studios, post-trained on MiniMax H3 and available inside PAI, Utopai's production platform, and now supports Image to Video!
Utopai X is the only model in the top 2 of both AA-Video-T2V v2.1 and AA-Video-I2V v2.1. On AA-Video-I2V v2.1, it is the highest ranked model built on MiniMax H3, ahead of MiniMax H3 (768p) itself, Hyperflow H3 and MiniMax H3 Max. In our testing, it does best on prompts that test Physics and Multi-Scene & Narrative, and on Frontier, UI/UX & Motion Design and Productivity & Knowledge Work use cases.
Utopai X is available in PAI at 93 PAI credits per second of 1080p video, with no public API. PAI plans start at $15 per month for 1,000 credits.
Congratulations to @UtopaiStudios on the release!
See below for our analysis of Utopai X in Image to Video 🧵
Mike Taylor@hammer_mt ·
Deep pull
- Artificial Analysis
@ArtificialAnlys ·
We've launched our HLE-Diamond leaderboard, our independent evaluation of the refined 1,000-question version of Humanity's Last Exam, as part of v5 of the Artificial Analysis Intelligence Index
HLE-Diamond was released by @CAIS and @scale_AI after a year of cleaning and refining the original Humanity's Last Exam dataset with input from the research community. It has 1,000 closed-ended questions, split evenly between reasoning and knowledge, and 272 of them include an image.
We run every question once without tools, using the official HLE system prompt. GPT-5.6 Luna (medium) grades each answer against the reference answer with the official HLE judge prompt, and we report the share of questions judged correct (pass@1).
Key takeaways:
➤ Only 17 model variants (~13%) score above 50% today: Gemini 4 Carbon (high) leads at 69%, followed by Claude Opus 5.5 (max) at 68%
➤ Models score higher on reasoning than on knowledge: Over 90% of the models we measured do better on the reasoning portion of the benchmark. Gemini 4 Carbon (high) leads both, at 80% on reasoning and 59% on knowledge. Claude Opus 5.5 (max) and GPT-6 Astra (max) both score 78% on reasoning, and Opus 5.5 is ahead on knowledge, 58% vs 50%
➤ Open weights models trail by more than 30 points: Kimi K3.1 Pro (weights not yet released) is the top open weights model at 37%, followed by MiniMax-M3.1-Flash at 28% and Kimi K3 (max) at 27%
Gergely Orosz@GergelyOrosz ·
A week from now, hosting two epic, free-to-attend events in the evening, in SF (registration required):
Wednesday (4 Nov): The Pragmatic Engineer Unplugged with @CompleteSkeptic & @bcantrill
RSVP: https://luma.com/pragmatic-engineer-unplugged
(cont'd)
nicksaraev@nicksaraev ·
AI has solved infinite personalized education.
This took $0.35 and ~15 minutes to make. You can create one on any topic, length, and level of complexity for a penny or two more.
Soon it'll be $0.00001 in tokens and realtime, next to you.
Human educational content is dead.
Max Stoiber@mxstbr ·
🫡
What should we ship next?
Ethan Mollick@emollick ·
Each of my posts has dozens of obvious and numbing AI slopreplies.
Worse, when humans really do reply (real example: someone from Anthropic addressing the issue I posted about), the "replies" algorithm does not surface them first
You can't really call this social media anymore.
Cognition@cognition ·
We've evaluated Gemini 4 Carbon and Gemini 4 Argon on FrontierCode 2.0, our coding benchmark that measure's a model's ability to go beyond the spec.
Gemini 4 Carbon scores near SOTA with a score of 40% and a cost of $4.04 per task, behind Claude Opus 5.5, GPT-6 Astra, and Claude Sonnet 5.5.
Gemini 4 Argon scores 26.3% with a cost of $6.41 per task, ahead of Muse Wonder 1.0, but behind Claude Haiku 5.5 and Grok 4.7.
See the leaderboard with full scores: https://cognition.com/frontiercode
Scott Wu@ScottWu46 ·
Really excited to work with this team!
- Ethan Mollick
@emollick ·
I had Opus build a game that takes JIRA tickets from any of your real projects & converts them into dramatic audiologs that are the plot of a 2000s-era horror game set on a mysteriously abandoned space station.
Play a demo: https://wontfix-game.netlify.app/ Github: https://github.com/emollick/audiologs
elvis@omarsar0 ·
Learn to build evals, folks!
You're not really doing recursive self-improvement (RSI) if you are not doing evals. And don't tell me your agents are generating the evals for you. They are pretty bad at it.
I have found it really interesting and useful to give agents a concrete way to measure results and stay on track with a long-term goal. It's easier to hill climb when things are clearly measured and well defined. Probably one of the most important skills anyone in the space can have today.
It doesn't matter how many agents and intelligence you throw at a problem; you are not going too far without proper evals.
- Gergely Orosz
@GergelyOrosz ·
This bus is so SF
A (secure?) vibe coding vendor paid for a bus ad
So did an AI code review tool
The bus company has so much AI ads demand it split the bus to 2x revenue
Theo - t3.gg@theo ·
This dude is the only person I’ve seen actively comparing actual dollar values and speeds across AI subscriptions in a realistic, accurate way. Instant follow.
- Mike Taylor
@hammer_mt ·
It's crazy but it's not wrong
Michelle Pokrass@michpokrass ·
oh yeah you probably haven't heard of my favorite micro brewery
- Artificial Analysis
@ArtificialAnlys ·
Search is one of the most important tools for agents, and they search very differently to humans. By the end of the year, agents are expected to search the web far more than humans do
The Artificial Analysis Search Index measures the performance, cost and latency of Search APIs across 1700 tasks and three search-specific benchmarks: BrowseComp, DeepSearchQA and AA-Omniscience using the same base model and harness.
Developers can use the Artificial Analysis Search Index to inform their choices when it comes to what search tool their agent should be using. Now you can see who’s #1 while waiting for the bus. Currently Exa (auto) and Perplexity (medium) lead in performances with their best models scoring 80.
- kache
@yacineMTB ·
Concerning
- Gergely Orosz
@GergelyOrosz ·
I’m honestly grateful for VC funding for this kind of crazy experiment and @jsnnsa for documenting it
It’s a wild read on what happens when you push SOTA AI as much as you can, in an environment where you let AI ship to prod where you can stomach if it breaks (eg this game)
- Theo - t3.gg
@theo ·
Ran the numbers on the plan changes for Codex, looking pretty good!
$500 plan nets to ~$10.5k/month of usage $200 plan nets to ~$4.3k/month of usage
jack friks@jackfriks ·
i know i've said this 100 times already but opus 5.5 is like stupidly unfair... like playing a pvp video game and using a cheat code no one else seems to know about
- Theo - t3.gg
@theo ·
More apps should come with the ability to debug themselves
- Gergely Orosz
@GergelyOrosz ·
Almost every dev who I asked “do you miss flow state (when coding)” told me they do and that with agents it’s different / not as satisfying
EXCEPT devs who are currently building agentic systems that AI cannot & there are zero blueprints, they need to invent it all
Hmm
Ryan Carson@ryancarson ·
I think this is where we’re all going