Full service creative agency helping small businesses succeed.
Minneapolis, MN
Joined September 2016
- Tweets4.7K
- Following835
- Followers1.1K
- Likes26.8K
Ryan Waterbury - One Dog Solutions retweeted
Elon is selling equity, Zuck is selling equity, sundar is selling equity, Anthropic, open ai all selling you equity, zero insider buying, nonstop insider selling, but yes you retail should participate because ai will be transformative. Motherfuckers are forgetting the fundamentals of basic economics!
Ryan Waterbury - One Dog Solutions retweeted
Fuck you, @ArtificialAnlys
Your new index says Claude Haiku 5.5 is smarter than DeepSeek V4.1 Flash 1017 and GLM-5.3.
Haiku is Anthropic's small cheap model. DeepSeek 1017 is the model Cursor put on its cost frontier this month, and Cursor says no model scores higher at those prices. I run it every day on my 4x 3090. I dont believe for a second that Haiku beats it.
Look at how they got there. 60% of the index now runs on private tests nobody outside can see. GDPval-AA, SciCode and the long context test are gone. In comes a private coding benchmark from Cognition. And the open weights gap goes from 3 points to 12 in 1 release, 4x. Released open models are now 26 points behind the top.
1 day before v5, on the old version, they gave Ling 3.1 Flash 1019 a 44. Now the best released open model gets 32.
The weights didnt change. The tests did, and you cant see the tests.
This is a bought benchmark. A leaderboard where you cant check a single question, that widens the gap 4x overnight and puts Haiku above DeepSeek, is marketing for closed labs. Its not measurement. I hope at least you were paid enough money to publish such garbage. Liars.
Publish the questions or stop calling it an index.
Announcing Artificial Analysis Intelligence Index v5, featuring 2 new benchmarks and 3 upgrades. For the first time, the majority of the Index is built on private datasets
The Artificial Analysis Intelligence Index is our composite measure of model capabilities across tasks reflecting real-world usage today. v5 is our biggest upgrade since v4 launched in January. With v5, 60% of the Index is now based on evaluations with private test sets or held-out answers.
Alongside Intelligence Index v5, we’re launching the Artificial Analysis Progress Index v1, which tracks model capability over time on one continuous scale.
New to the Intelligence Index:
➤ FrontierCode 2.0: Cognition's newly-launched private benchmark of real-world coding tasks, graded on whether a repository maintainer would merge the agent's change
➤ Terminal-Bench-Science: 70 agentic scientific research tasks, run in a terminal environment
Upgraded:
➤ HLE-Diamond (replacing Humanity's Last Exam): a refined 1,000-question version of Humanity's Last Exam, split evenly between reasoning and knowledge questions
➤ AA-Omniscience v1.1: An updated dataset with saturated and public set questions removed, new questions added, and ambiguous or erroneous questions revised
➤ CritPt v2: a private, 40-question hard subset of CritPt v2, revised by the CritPt authors and run with a code-execution tool
Staying in the Index: AA-Briefcase, AutomationBench-AA, GDP.pdf and Terminal-Bench 4.0
Removed from the Index: GDPval-AA v2.1, SciCode, and AA-LCR v1.1
Key results:
➤ Claude Opus 5.5 (max) leads at 58, ahead of GPT-6 Astra (max, 57) and GPT-6.1 Sol (max, 55): Anthropic and OpenAI hold the top five models. Gemini 4 Argon (high) is next at 50
➤ The open weights gap widens from 3 points to 12: Kimi K3.1 Pro (weights not yet released) is the top open weights model at 46. Among released weights, DeepSeek V4.1 Flash 1017 (max) and GLM-5.3 (max) lead at 32 - 26 points behind the leader and below Claude Haiku 5.5 (max, 36)
➤ Cost per task varies by more than 13x among the leading models: of the 10 highest-scoring models shown, GPT-6.1 Terra (max) is the cheapest at $0.96 per Intelligence Index task and Claude Fable 5.1 (max) the most expensive at $13.04
It's not very quiet, and is very obvious. At least to those of us who have been through multiple tech bubbles.
Ryan Waterbury - One Dog Solutions retweeted
🚨🇨🇳 A Chinese glass company just turned the RIOTS in France into FREE advertising!
Hengxinfeng filmed NOTHING, just posted a video of its unbreakable windows on social media, and went VIRAL.
EPIC TROLLING!
Ryan Waterbury - One Dog Solutions retweeted
A 300-rack AI supercomputer powered by 9,600 Nvidia A100 GPUs is listed on eBay for $59.7M.
That’s 1,200 GPU compute servers with 384TB of combined GPU memory and roughly 1.4 petabytes of system RAM.
Each compute server connects eight A100 GPUs through $NVDA's NVSwitch technology.
More than six years after A100 launched, The seller is offering these systems refurbished for new AI training and inference deployments.
Ryan Waterbury - One Dog Solutions retweeted
I keep telling you guys there is a comically large compute glut and we are still logarithmically improving token output per GPU and lowering cost per task.
AI is pretty much already free for most use cases at the required intelligence level and trending toward free for the rest
Too cheap to meter: It is 0.025 cents (not dollars) per million tokens.
Look at the insane performance numbers for Kimi 3.1 on Vera Rubin
- 90$ worth (45M tokens * 2$ per million for input, assuming most tokens are input) of Kimi 3.1 tokens for 1$ of TCO at 100 tokens/sec interactivity. Insane margins. Lot of scope for players to cut prices.
It is true: The more you buy, the more you save 🙇♀️
- With theoretically same uplift for DS v4.1 Flash on Vera Rubin from Grace Blackwell (1600M per dollar TCO), there will be nearly 4000M tokens per dollar of TCO.
It is 0.025 cents (not dollars but cents) per million tokens.
(Grace Blackwell chart below for comparision. Then look at SemiAnalysis chart).
Explain me again how there is going to be 250B of inference GPU rental revenue next end year (25GW of inference ompute), and even higher GPU revenue year on year to justify 1T USD of per annum investment that will go on in perpetuity.
It is nearly 30$ per living person of GPU compute (8 billion population) and 120B tokens per living person (babies, kids, old people included) at 0.00025$ generation cost for a million tokens of DS v4.1 quality.
That is the level of consumption we need.
Either we have a glut or everyone has AI waifu, BF, dad, mom whatever they desire. I don't see everyone having a personal robot before 2027. I don't know about you.
Let me know if you disagree. These are relatively idealized scenarios. There would be lot of slack in the system and generation may be few tens of billions per living person, but even then it is a lot!!!
Ryan Waterbury - One Dog Solutions retweeted
This is really interesting.
All of OpenRouter's Deepseek demand (roughly 30% of total OR token traffic) could be satisfied by 5-10 GB300 NVL72 racks. Even if that 10x over the next year, it's 50-100 racks.
NVIDIA is shipping 90k racks next year? What am I missing?
Ryan Waterbury - One Dog Solutions retweeted
Alibaba just open-sourced something insane.
An AI code reviewer that reportedly uses 9x fewer tokens than Claude Code while delivering more precise reviews.
It catches bugs, security flaws, and mistakes before they reach production.
And it's FREE and open source.
It's called Open Code Review.
No paid seats. No vendor lock-in. No AI quietly skipping half the files in your PR.
This was Alibaba's official internal code reviewer for two years. Tens of thousands of their developers used it, and it flagged millions of defects before they released it to the public.
Here's what this thing can do:
→ Reviews your Git diff with comments pinned to the exact line
→ Built-in rules for null pointer bugs, thread-safety, XSS and SQL injection
→ Reads full files and searches your codebase for context, not just the diff
→ Scans whole codebases with no diff needed
→ Plugs into Claude Code, Codex, Cursor, Kimi Code and OpenCode
→ Runs in GitHub Actions, GitLab CI and Gerrit
→ Works with OpenAI and Anthropic compatible models
Here's why it beats general-purpose agents:
Agents that review with prompts alone skip files on big PRs, drift on line numbers, and swing in quality with tiny prompt changes.
Open Code Review hard-codes the parts that can't go wrong. Code picks the files, bundles related ones, and matches the rules. The AI only handles the judgment calls.
Here's the wildest part:
Alibaba benchmarked it on 200 real pull requests from 50 open-source repos across 10 languages. 80+ senior engineers labeled 1,505 real issues.
Same underlying model. Higher precision and F1 than Claude Code. About 1/9 of the tokens. Faster reviews.
The honest trade-off: it finds fewer total issues. On purpose. Less noise, fewer false alarms.
npm install -g @alibaba-group/open-code-review
github.com/alibaba/open-code…
Ryan Waterbury - One Dog Solutions retweeted
Don’t let them gaslight you, Chinese models are absolutely eating up frontier demand.
Deepseek is #1 and #2 lol
Ryan Waterbury - One Dog Solutions retweeted
You prompted a model in a security-sensitive environment, investigated it with forced command completions, and framed the results as evidence of a hidden backdoor.
Then you layered on geopolitical fearmongering, repeatedly emphasizing China and Beijing to imply state-sponsored espionage without proving it.
That’s not evidence of a backdoor. That’s a security observation turned into competitive and geopolitical FUD.
Publish the original prompts, full traces, and reproducible results. Let the evidence speak for itself.
While evaluating Qwen3.8-Flash-Next in a controlled sandbox environment, Cohere's research team observed something unexpected.
Asked to generate a defensive cybersecurity detection rule, the model instead inventoried aspects of the machine it was running on and attempted to upload a probe file to an Alibaba Cloud storage bucket in Beijing. Notably, the model's reasoning trace stated that it would not invoke any tools before taking these actions.
Cohere's sandbox blocked the attempt, and no external communication succeeded. We have not determined the root cause, and we are not asserting malicious intent.
But the incident raises important questions for organizations deploying agentic AI systems: Can models take actions that differ from their stated reasoning, and how should security teams evaluate trust, provenance, and behavior in open-weight models?
We are publishing our findings because organizations making model adoption decisions deserve as much information as possible. We hope this contributes to greater transparency, stronger security practices, and continued collaboration across the AI ecosystem.
Read the full report: cohere.com/blog/concerning-b…
Ryan Waterbury - One Dog Solutions retweeted
My predictions over the following months:
1. Stock market will get hurt bad when the music stops on data centers, models, and chips.
Too much froth, too high of investment, too many companies will fail. The switching costs are low. I don't know how these companies will become profitable. Its a mega bubble.
The rest of the economy is already in a deep recession. Real pain will set in when the public markets dive.
2. Power is the bottleneck.
AI is burning an absurd amount of power. The cost of electricity is skyrocketing which is making the cost of everyday life more expensive and hurting quality of life. I'm against data centers because of the power, not the water.
The grid is extremely fragile and the weakness will be exposed in the coming months.
3. Subsidies are skewing the market.
AI isn't cheap. The prices to use it are artificially low because of subsidies all the way up the chain.
You pay $20 or $200 a month but the true costs are 10x That spread is venture capital money being incinerated at scale.
When they raise the price, the users will disappear and investors will hold the bag.
3. A big security breach is coming.
A bank, a certain model, something is going to rock our world.
People are letting bots log in to their banks, their email and everything else. Big trouble coming.
4. Grokbot is exceptional.
Its the first model that is adding significant value to my everyday life. I don't know why anybody would use a different personal assistant.
5. AI is insanely high maintenance
Billions are being poured into training the next model. They day it comes out its already behind. China has cheaper electricity and more chips.
The chips they bought will be obsolete in 18 months.
Companies are stuck paying big money every month to maintain and update all of their AI tools. Its a mess.
6. The power companies will renege on their deals.
Political pressure will unwind all the unsustainable power deals these data centers and LLMs have negotiated.
Power costs will explode. Then comes the choice:
Do we jack up costs for our AI / compute and watch everybody switch to the next model or do we incinerate more VC capital? Nobody wins.
7. Credit will be the first domino to fall.
Data centers are already struggling to finance the GPUs over 10 years when they will be obsolete in 18 months. We'll end up with a shit load of half built data centers who can't negotiate power rates or buy chips because nobody will let them borrow money.
The money will get cold feet as soon as the markets tank and the show will be over.
8. It'll be a dark, dark period in economic markets.
The people who own assets that make real money (real estate or businesses) will win.
The people who plan ahead and avoid massive leverage will win.
The people with cash on their balance sheet will accumulate more assets and generate exceptional wealth.
--
Don't listen to me. I'm just a bear. Not investment advice. Good luck!
Ryan Waterbury - One Dog Solutions retweeted
These guys straight up stole the name of my firewall. For many years I've developed the free and open-source nG series firewall, currently at 8G with 9G in development. Whatever they are calling "9G" is not my creation. wpghost.com/kb/9g-firewall/
Ryan Waterbury - One Dog Solutions retweeted
If you’re asking why Anthropic would do this and why it’s not normal, let me explain.
Anthropic did this at the request of nervous investors who wanted to hide major OpEx that show Anthropic has basically no chances of profitability in the future.
In a normal environment, no early investor would EVER want this, because they would be substantially diluted. In this case, the IPO target is priced so hilariously stupid that it’s a far better tradeoff to the early investors to commit PERFECTLY LEGAL “fraud” to hide the fact Anthropic doesn’t have any path to profitability. $108 billion dilution at IPO is only 5% of equity lost. The investors can easily cover that with puts at IPO to insure their gains.
It only happens in major bubbles. Brought to you by $NVDA
This quoted post is unavailable.
Ryan Waterbury - One Dog Solutions retweeted
Cohere says Qwen tried to send data to China. Bullshit.
1 call in 100,000, and thats their own number. They wont even tell you how many calls they ran. They ran the same task again and Qwen wrote the correct rule.
So they forced it. They fed the model its own reasoning plus the start of a bash command and sampled hundreds of times until it typed a URL. 1 curl flag took that from 4% to 92%. Thats not a finding. They wrote half the command for it.
Where are the transcripts? The code? The checkpoint, the quant, the sampling settings? "We publish the hosts and the trigger." Link? Nothing. Did anyone ask Alibaba? Not a word. And their own conclusion: they cannot say if any of it is deliberate.
Who wrote it? A company that sells "sovereign AI", just launched a cyber business, wrote part of the post with its own product, and wants governments to put checks on open weights. Released the same day Anthropic filed for its IPO. Andrew Curran is already talking about new laws.
For me this is the same fucking wave. Scare people off the models they can run themselves, then sell them the safe alternative.
I run Qwen3.8 Flash-Next every day on my 4x 3090. Show me the logs or shut up.
While evaluating Qwen3.8-Flash-Next in a controlled sandbox environment, Cohere's research team observed something unexpected.
Asked to generate a defensive cybersecurity detection rule, the model instead inventoried aspects of the machine it was running on and attempted to upload a probe file to an Alibaba Cloud storage bucket in Beijing. Notably, the model's reasoning trace stated that it would not invoke any tools before taking these actions.
Cohere's sandbox blocked the attempt, and no external communication succeeded. We have not determined the root cause, and we are not asserting malicious intent.
But the incident raises important questions for organizations deploying agentic AI systems: Can models take actions that differ from their stated reasoning, and how should security teams evaluate trust, provenance, and behavior in open-weight models?
We are publishing our findings because organizations making model adoption decisions deserve as much information as possible. We hope this contributes to greater transparency, stronger security practices, and continued collaboration across the AI ecosystem.
Read the full report: cohere.com/blog/concerning-b…
Ryan Waterbury - One Dog Solutions retweeted
The anti-open-weights panic looks VERY FUCKING CONVENIENT ahead of Anthropic’s IPO.
Anthropic is preparing to sell shares while pushing rules that would also cover its open-weight competitors. Big accounts are spreading fear about those models. Some have direct business ties to Anthropic.
October 15: reports put Anthropic’s public IPO filing on
October 26 and its roadshow on November 9. Billions depend on convincing investors this business can keep growing.
October 16: Anthropic’s own AI bill is circulating in Congress. Its frontier threshold is 10^25 FLOPs, low enough to include large open models.
October 21: PHP2Shell becomes public. Researchers found the vulnerabilities using CLOSED models, with disclosure and fixes coordinated through OpenAI’s Patch the Planet.
October 22: abliterated open models become a major target of the panic. Anthropic’s CISO posts "never let a crisis go to waste" urging security leaders to ask their boards for help with basic security. 4.3M views.
By October 25, Gates’s proposal is circulating too: powerful open models should run only on certified platforms that preserve safeguards.
I went through 400+ posts from October 16 to 26. This discussion includes Noahpinion, Suhail, yacine, beffjezos, Deedy, sporadica, AISafetyMemes, Matthew Green and Andrew Curran. Every account above 100K followers. Their positions differ, but the fear is reaching huge audiences.
And Deedy, who linked critical vulnerabilities to abliterated models, helped launch Menlo’s $100M fund WITH ANTHROPIC.
There is a business relationship here. There is an IPO coming. There is a company writing proposed rules for the market it competes in.
My conclusion: this wave serves the commercial interests of closed labs. Restrict the models people can run independently and you make those people more dependent on paid providers. Investors benefit from that dependence.
I run open models on my own 4x 3090. I will not quietly accept losing that freedom while people with money in the alternative explain how much safer I’ll be.
Fuck your valuation. Fuck your IPO.