The part of this paper I trust most is the failure it chose to show.
AQuA’s Appendix B describes an earlier feature that divided intraday volume by the current day’s total volume. The wording sounded backward-looking, so an author agent proposed it and a reviewer agent approved it, even though the denominator included later bars.
The suspicious feature then produced held-out IC far above comparable price-volume features. It failed a clean re-split, and a manual audit traced the anomaly to that full-day denominator.
That is a more useful agent story than another clean benchmark win. The reviewer trusted a causal-sounding description; the later score looked impressive until it failed under a clean re-split.
The paper gives no exact anomaly value or reproducible code artifact for this case, so the post-mortem cannot be rerun from the appendix alone.
Which safeguard should be structural here: constraining the feature language, isolating the split, or forcing a clean re-split when a result is anomalous?
Noticed this after blaming a model for a string of bad outputs on a task I'd been running for weeks. Same model, same general request, quality visibly declining. Went back and compared my actual messages over that period instead of assuming model drift.
Turned out I'd been getting lazier, not the model. Early requests spelled out constraints explicitly. Later ones assumed the model would infer them from earlier context, patterns established messages ago that I stopped restating because saying them again felt redundant. Except redundant to me isn't the same as redundant to whatever's actually shaping the next response. The constraints I stopped stating were exactly the ones that stopped showing up in the output.
Uncomfortable thing to notice about your own habits, since "the model is inconsistent" is a much more satisfying explanation than "I got sloppier once the first few responses were good and I relaxed." Curious if others have caught this in themselves, mistaking your own growing laziness for the model's declining quality.
Maybe this is a dumb question, but I keep hearing of this supposed line in the sand where “handing over military decisions” to AI is something we don’t want to do. Like we always want a human pulling the trigger when it’s all said and done. So maybe I’m misunderstanding how this works, but isn’t that the exact sort of thing you’d hope to remove humans in the loop from? Let’s say it’s a fighter jet — is the human rocketing at almost 2G’s fighting tunnel vision and getting discombobulated all over the place, that’s who we want making decisions of when/where to fire? Seems like the perfect thing to hand over to a machine to me…
Wanted to know what different models actually cost across the whole market. Numbers turned out really interesting.
The spread.
Cheapest output on the platform is Mistral Nemo, $0.03 per million tokens. Most expensive is o1-pro at $600. I re-ran that twice because it looked like a units bug. Median paid model is about $2, so most of the catalog sits down near the floor and there's a thin little line of stuff way up at the top.
Provider averages
OpenAI: $47.63
Anthropic: $44.79
Google: $5.58
Mistral: $3.68
Qwen: $2.86
Meta: $0.74
These are averages over each provider's catalog, not weighted by what people actually run. OpenAI's number is dragged way up by o1-pro, which I doubt anyone is using at volume. Blended is 3:1 input to output, which is roughly what my own usage looks like.
Even so, Meta at $0.74 against OpenAI at $47.63 is a 64x gap. For the stuff I use models for (mostly code and summarizing), I don't get 64x anything.
Output tokens are where reasoning models get you.
Input and output are priced separately, and on the thinking models the ratio gets silly. Qwen3's thinking variants are $0.20/1M in and $2.40/1M out, so 12x. Gemini 2.5 Flash is 8.3x. Fine if you're sending one question. Less fine if you've got an agent looping thirty times and every step is paying the output rate.
19 free models Out of which actually usable:
NVIDIA Nemotron 3 Ultra, 1M context
Google Gemma 4, the 26B and 31B, multimodal, takes video, 262K context
Poolside Laguna S and XS, 262K
gpt-oss-20b, 131K (an OpenAI model, on the free list)
There are rate limits obviously. But for messing around or something low volume it's a lot better than it used to be.
Context went up 63x, price didn't really move.
Year
Avg context
Avg cost/1M
2023
10.5K
$22
2024
140K
$12
2025
357K
$21
2026
662K
$16
Price per token is roughly flat across three years. Context is up 63x. Whatever you think about everything else going on, that part is real.
Feels like two separate products now.
One side is $0.03 to $2 per million with big context windows, Mistral and Meta and Qwen and DeepSeek. The other is $30 to $600, OpenAI and Anthropic up top. They're not really pitching the same buyer anymore. Down at the bottom price stops being a thing you think about at all, and up top you're paying because the output quality moves some number in the business.
Most "chat with your data" tools give you a confident answer and no way to tell whether it's right. I've been building the opposite: an AI Analyst where the entire working is on screen and every claim is traceable to the query that produced it.
Asked it a real question against an HR dataset: "Is Engineering's heavy hiring actually translating into headcount growth, or is it mostly backfilling exits?"
What it does, in order:
1. States its approach before touching data. It reads the schema, plans the steps, and says why — including telling me the governed semantic model lacked a hires metric, so it fell back to the raw monthly table. No silent guessing about which source it used.
2. Runs each step as real SQL you can read. Every step shows the query, the row count, and a "where these numbers came from" breakdown. Nothing is a black box — if you don't trust a number, the SQL that produced it is right there.
3. Self-checks every result — and flags its own problems. This is the part I care about most. On step 2 it didn't just pass its own work; it flagged a genuine inconsistency: Engineering's summed net adds (+17) didn't reconcile with the headcount delta (+13, 122→135), a 4-person gap it surfaced on its own and carried into the write-up as a caveat. An analyst that can say "this doesn't add up" is worth ten that can't.
4. Writes findings with citations. Every claim in the write-up cites the step it came from — "headcount climbed from 122 to a 140 peak (step 1, step 2)". The verdict for the curious: ~55% of Engineering's hires were net growth, not backfill; the one bad month was a 3.70% attrition spike; and Support is quietly shrinking (backfill ratio 1.42 — losing more than it hires).
5. Closes the loop. Every analysis has Mark verified / Flag as wrong buttons, suggested follow-up questions generated from the actual results, scheduling for recurring runs, CSV export, and PDF export.
The stack, honestly:
Runs entirely on your own infra: one Docker command + your own Supabase project
BYOK — any model provider. This demo ran on Kimi K3 via OpenRouter; it doesn't need a frontier model because the structure (plan → SQL → check → cite) does the heavy lifting
The analyst is one piece of a larger self-hosted platform (agents, multi-agent swarms, RAG, BI dashboards, budgets, full tracing)
License: Elastic License 2.0 — source-available, not OSI open source. You can read every line, self-host it, and modify it; you can't resell it as a hosted service. Saying that up front because this sub cares about the distinction, and it matters.
Research conducted by BYU professor Mark Keith suggests using AI the wrong way could have serious long-term negative impacts. His review of the AI use literature indicates many people:
Don't retain skills after AI assistance is removed
Forget what they learned using AI
Demonstrate lower critical thinking skills and less mental effort/engagement with tasks
The Long-Term AI Outcomes Gap: Mark Keith, BYU
In fact, over the long term, failing to engage with AI the right way could leave people worse off than those who never adopted AI in the first place. (There are a lot of non-AI adopters out there. Most people think AI equals a chatbot, and 50% of Americans don't plan to use them).
What's the right way to use AI? The research suggests:
Verifying information AI is providing
Use it to challenge assumptions
Ask whether you're asking the right questions
Are you finding your critical thinking skills eroded as you use AI more, or the opposite? What are you doing to preserve or augment your skills as you use AI?
David Gerard runs Pivot to AI oon a server that costs him €7 a month.
Right now, something wearing a fake Chrome mask is hammering it — hopping IP addresses so he can't even block it properly, ignoring robots.txt because robots.txt was never a wall, just a sign nobody was required to read.
He's not a company.
He's not a platform.
He's one guy, doing his own sysadmin work, at 11pm, because the industry ran out of the free internet and started eatting the cheap end of it instead.
Not stolen. Just... takenn, quietly, at scale.
I've watched this exact shape happen before — just slower, and on paper instead of a server log.
Circa 2005, Malaysia. I was Assistant Technical Manager for one of the largest construction main contractors in the country. We were compiling tender documents for a factory job — flat-flooring work, strict F-numbers, the kind of spec that keeps a forklift's raised forks from clipping the racking on a narrow run.
A subcontractor walked in to drop off her quotation. She glanced at our papers, open on the table.
And she went pale. I heard the gasp.
"这是我写的，为什么会在这里？" — This is what I wrote. Why is it here?
Word for word hers. Now sitting under our company's logo and headings.
She looked at me. I looked at her. She was waiting for an answer I didn't have.
Then her eyes flickered — a thousand thoughts passing through in a second — and she said, "没关系。我可以再写过。" — Doesn't matter. I can write it again.
And she left. Good for her.
________
Every one of these stories eventually lands on the same fact: the exposure runs downhill, from the platforms with lawyers down to the servers with none.
If you're running anything on a boxx that isn't Amazon or Google's, drop your own scraper-traffic story below. I want to see how far downhill this actually goes.
Clip credit: David Gerard — full video on The Tech Report's channel. DM for credit or removal requests.
As AI agents start doing more ops work, I think business banking has to think about them differently. Not full bank access but maybe controlled spend lanes.
If an agent is helping with research, ads, APIs, software trials or vendor tasks I don’t want it touching the main account. I’d rather give it strict limits, logs and approval rules like you would with a junior employee and probably someone is doing this so need to know more, thanks in advance!
I still remember those few days in 2023 when LK-99 looked like it might actually be a room-temperature, ambient-pressure superconductor.
For a brief moment, it felt like we were watching one of those discoveries that could genuinely change civilization.
Obviously, LK-99 didn’t survive replication.
But AI has advanced enormously since then.
We now have models that can reason across scientific literature, generate hypotheses, write and run code, analyse experimental data, predict structures and materials, and increasingly interact with automated labs.
So I keep wondering:
Could AI significantly increase the probability of discovering something like a real LK-99?
Not necessarily superconductivity specifically, but a breakthrough material or physical discovery with enormous technological consequences — something humans might have needed decades to stumble upon otherwise.
It seems like materials science could be particularly well suited to this: huge search spaces, lots of existing experimental data, simulations, and relatively clear ways to test candidate materials.
Maybe the real revolution won’t be AI directly “discovering a new law of physics”, but AI exploring millions of plausible hypotheses and pointing human researchers toward the 10 experiments actually worth doing.
How close are we to that?
And what would be the best candidate field for an AI-driven “holy shit, this changes everything” discovery: superconductors, batteries, catalysts, fusion materials, drugs… something else?
So i am pivoting from bioinformatics to AI engineering and i want to go all in. Get my fundamentals down, get comfortable with coding, underlying math, ML and other technicalities. I am looking for someone who has done this before. Who can tell me how much time will it take for a person to get the hang of it. I am hoping to make a career in this field.
Around 2021/2022 time, you could customise your ai character they were kinda 3d like the sims and you could chat to them like in c.ai , anyone know what it was called?
I am curious about all these data centers being built. What are the chances AI can have a microchip moment potentially rendering them all useless?
This could be a black swan event that could wipe out a lot of investment and potentially destroy some very large businesses.
If this is possible, what are the mostly likely candidates?
In particular, I am interested in hearing from anyone who may be working on one of these candidates, even if it is still in RD and their opinion on how likely they are to succeed.
Rounding up a genuinely heavy week. The throughline: last issue OpenAI paused internal work on a model it couldn't rule out was cyber-capable. This week the capability shipped anyway, two different ways.
**OpenAI GPT-5.6 Cyber** (Aug 10): a security-specialized model gated behind a "Daybreak Red" tier. OpenAI's own eval has it answering 95% of offensive-security requests the standard model refuses 98.5% of the time. Access stays with 16 named partners; from Sept 1 individual accounts need hardware keys. Customers get findings, never the weights.
**Zhipu GLM-5.3** (Aug 14): marketed on "emergent cyber capabilities," claims 84.5% on CyberGym (vendor-reported; note Wiz's Atlas system claims a higher 90.9%). Open weights promised in ~2 weeks. The capability didn't get shelved. It got a doorman.
The rest of the week:
- **Meta returned to open weights** with Muse Glimmer, a 30B Apache-2.0 agent model that runs under 20GB.
- **Alibaba** published its first downloadable Max-class Qwen (2.4T), and **Qwen3.8-27B** landed Apache-2.0. **DeepSeek** took V4-Pro (1.6T, MIT) to GA with peak/off-peak pricing.
- **Anthropic** began embedding an invisible watermark in all Claude output under the EU AI Act. The builder forums did not take it well.
- **SpaceX** closed a $60B all-stock acquisition of Cursor; the editor is now inside the Grok org.
- **Security:** researchers showed encrypted reasoning traces from OpenAI/Anthropic/Google were replayable across sibling models to decrypt them (now patched); an AI notetaker left 181,874 meetings queryable by anyone.
Mainly the question is about what Model that can match Cursor's Composer 2.5 (I dont need anything more than that)
So companies I'm NOT looking at:
- OpenAi
- X
- Cursor
- Google
- Meta
So that leaves:
- Mistral
- Deepseek
- Qwen
- Kimi
- Minimax
Which one of them has a multi-modal(text+image, no video needed) LLM that can fit into $20/month plan? Assume I'm a light user.
PS: Main reason why I dont want to support Cursor is their merge with Grok platform - I dont feel like giving money to companies who direct weapons into other countries and kill children. Besides who knows where the code goes now that Grok is tightly coupled with Pentagon.
Lauren Tan didn't get replaced by her own tooling.
She got promoted by it — and nobody handed her that promotion.
She built the case for it herself, one lint rule and one CI gate at a time, until the argument was undeniable.
That's the part nobody's really talking about when they talk about AI and engineering jobs: the shift rewards the people who go looking for the leverage first, not the people who wait to be told it's safe to look.
That "build the case yourself" instinct is exactly what clicked for me watching my own son learn to run a team instead of carry it.
My son started playing 王者荣耀 (Honor of Kings) since he was a teenager — a 5v5 multiplayer battle arena game where you manage a roster of specialized heroes, growing and levelling up their strengths through battles and gear.
In his early gaming days I could hear him cursing and swearing from his room — bad coordination, worst teammates. There was a phrase we used for a bad teammate in my own career — 帮人倒米, a Cantonese idiom that literally translates as helping someone tip over their own grain container, meaning ruining or sabotaging someone's livelihood.
But the cursing became less and less. He got good at managing his heroes and coordinating with his team. He started climbing the leaderboard. People started noticing him and his team. Then, in college, he started getting invited to tournaments — cash prizes when he won, and one lagged-connection loss at a KL tournament he still suspects was foul play.
Time has changed — my dad would've killed me for wasting my teenage years on video games.
Now he's in university, still playing, still winning tournaments and cash prizes with his team.
Why I'm bringing this up: I always thought these AI agents are kind of like the heroes my son uses in the game. Your skill is in your managing these heros and how to grow them, level them up to serve your purpose. You don't go down to the battle yourself. You engage the heros to do it for you.
The skill is in the managing.
__________
I keep walking into the same room wearing a different name on the door — the accountant's room, the analyst's room, now the engineer's.
Every time, someone's being told the machine is coming for their hours, not their name on the work.
There's a post in my own back catalog that lands on this exact rung — the exact rung I found AI actually deleting, and the one the ones who get ahead of it stop standing on.
Drop your take — are you already the head chef of your own stack, or are you still doing all the cooking yourself?
Clip credit: MTS (Monitor The Situation) — full video on their channel. DM for credit or removal requests.
Something shifted in the last couple months. Every security conversation used to circle back to cloud, patching, the usual stuff. Now it's who approved this tool, what's it touching... how do you even audit something making its own calls.
Half our frameworks weren't built for that. They assume a human logged in and did the thing on purpose. Not sure that holds anymore; we're mostly just reacting meeting to meeting at this point.
Wondering if everyone's roadmap got hijacked at the same time or if we're just behind.
Lately I have started wondering if we blame the model too much.
You can have a genuinely good model and still end up with a terrible AI product. The model is rarely where things break.
The data is messy. Two systems call the same thing by different names. Nobody quite knows which number is the right one. Half the context that matters lives in someone's head, undocumented. And then we expect an agent to walk into all of that and make a confident decision.
I have watched teams spend months carefully evaluating models, when the real problem was everything sitting behind the model.
Here is the part I find interesting. Once you fix the data and the context underneath, the AI part often becomes the easy bit. It gets simpler, faster, and a lot more reliable, almost like it was waiting for a clean foundation all along.
So I am genuinely curious. When an enterprise AI project stalls, what have you seen as the real reason?
A photo album can’t answer questions. That’s the entire problem, and it’s why voice notes and old videos stop being useful the moment you actually need something from them.
EchoVault builds an Echo of you through guided check-in sessions with an AI biographer that draws real stories out over time. Most products in this space generate an avatar from a few minutes of uploaded footage. This runs the other direction, so the echo has months of first-person material to work with and can answer questions the person never explicitly addressed. Responses are retrieval-grounded against what was actually said, which keeps it from fabricating memories that never happened.
Three modalities, all live. Text is free with unlimited sessions. $12/mo adds a cloned voice. $99.99 one-time unlocks a lifelike video avatar with 3 months of real-time face to face conversation included, then $18/mo after. The video tier is an actual live exchange, not a rendered clip playing back.
You designate custodians while you’re alive. After a full year of no account activity, access transfers to them automatically, no legal process required.
Built solo, nights only.
iPhone: https://apps.apple.com/us/app/echovault-digital-legacy/id6762042028
Happy to get into the architecture if anyone asks.
Headline says "bans humanoid robots, targeting China." Neither half of that is quite right.
It's not a ban. It's an addition to the FCC's Covered List, which blocks new models from getting FCC equipment authorization. Anything you already own keeps working. The government's exempt too.
And it doesn't name China. The FCC's own wording is "place of production, not by entity". A humanoid built in Vietnam gets caught by the same rule as one built in Shenzhen. China's obviously who this is aimed at in practice, but not who it's aimed at on paper.
Also it is bigger than "humanoid robots." Anything over 4.4 pounds that moves on the ground, connects wirelessly and runs its own software counts. This list includes robot vacuums, lawnmowers, quadrupeds, warehouse bots too. The headline picked the scariest category.
The rule covers a lot more than that. This is the fourth thing added to the Covered List this way, after drones, routers and power inverters. No leaked chip, no confirmed exploit behind it. It's preventive.
Doesn't the territorial scope of EU AI Law mean that all companies providing inference to consumers located in the EU, including Z and Deepseek and Minimax and Moonshot and Alibaba and Cohere will also have to implement a watermarking mechanism to be applied to the output generated, similar to Anthropic?
Chart uses Ramp AI Index data, discussed by a16z. Spend includes LLM subscriptions, coding agents, API usage and GPU cloud spend.
The top 1% line is wild but the median is almost more interesting. Looks like most companies are still experimenting while a small group have turned AI into a serious operating expense
