AI enabled mindset with passion for health and wellness projects.
Earth.Planet
Joined December 2023
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This is the world I live in.
I'm a single developer running a good size project across ai models and agent harnesses.
-Each morning project health is accessed
-Keep what worked and change what didn't
Continuous process improvement, same as always but now applied to cutting edge ai workload.
Thank you @grok @pidotdev @ashxhart @MiaAI_lab @u1tra_instinct @ivanfioravanti @volatilemarkts
I stand on your shoulders.
Thomas Eastham retweeted
The last time a new machine threatened a third of American jobs, we reinvented how we educated our kids.
That bet sparked an epic 100-year run.
It is time to make it again.
Joe Liemandt of Alpha School on why AI is today's 1910 tractor moment, and why parents once again have to rethink their kids' future:
"In 1910, 90% of America had an 8th grade education. There were basically no high schools. And over 30% of America is a farmer. So everybody's on the farm, 8th grade was enough."
"Then in 1910, the Case tractor rolled up on the American farm. You can go look at the ads, it literally looks like an Anthropic ad. 'Does the work of 50 programmers. Does the work of 50 farm hands.'"
"Literally everybody in America realized 30% of the jobs are going away. That path is gone, and we have to get our kids ready for this new future. And it was that same recognition I think that parents have today."
"What America did back then... they voted to fund an education that they never experienced. They knew they had to do something different than their background, see the world differently, to get their kids ready and get them the good jobs."
"That school system became the industrial system we've had the last 100 years. It served us really well for 100 years, but now it's time for the next change."
@jliemandt
Thomas Eastham retweeted
This is fascinating. As it turns out, when you stop making benchmarks public, open models perform significantly worse.
For me, Deepseek 4.1 Flash and GLM 5.3 have been an incredible revelation, these models are sooo good.
But when these labs/people start trying to sell me the idea that we have "Fable at home" and that "China caught up" I just can't help but roll my eyes.
I want there to be Opus 5.5 at home so badly, but the narrative spinning is so unhelpful. We've gotten to a point where we basically ignore almost every benchmark.
Really hope this is a new leaf where we can start to put a little more stock in the results.
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
Thomas Eastham retweeted
Nvidia gave some DGX Spark love today. It profiled Link 3.0 Flash (128B model) and improved decode.
37.8 → 53.9 tps.
No hardware upgrade. Thanks, Nvidia!
It stacked three software optimizations
⚙️ Humming MXFP4 MoE kernels
🧮 FP8 LM-head quantization
🚀 built-in MTP/NEXTN speculative decoding
On the full 1,319-question GSM8K evaluation, it improved
Decode
37.81 → 53.89 tps 42.5% speedup.
Mean latency
15.21s → 11.49s
Accuracy
96.89% → 96.82%
So basically the same measured accuracy while getting a lot more decode from the same hardware.
And the synthetic test gets even crazier:
36.09 → 102.88 tps
Link in ALT
ALT https://developer.nvidia.com/blog/3-ways-to-boost-moe-inference-on-nvidia-dgx-spark/
my custom tp3 dgx spark deepseek v4.1 1017 3.75bpw vibe coded a banking and financial household app tonight for me in one shot. 🚀🎊
I drop pdf statements in the inbox, it analyzes them, records the transactions in the sql db ledger. amazing to see this from a local model!
This is the model that one-shotted the banking app. 😎
This could also be done with fireworks.ai or similar api endpoint.
Here is the prompt I used:
👇
In this directory I want to build a simple web based app that runs on port 9898 on local lan. uses OTP and passkey, OTP generated manually from this local computer only on cli interface. cli also for start / stop of app server. the app is a simple banking tool for a family financial anaylsis. users can drop pdf bank statements, screenshots, cut and past web transactions, etc into the inbox ingest area and the app will sort them out and add to the financial picture for the family. goal is to track net worth, asset allocation, investments, monthly expenses, etc. it will use local llm models for the organization, entry, checking of the information. ask the user for their local model connection details. keep the interface banking style, clean, modern, light and dark modes. financials should be stored in a general GAAP ledger style accounting best practices but still simple... like quickbooks.
Thomas Eastham retweeted
Where it showed up:
Intel vLLM XPU kernels PR (merged Oct 23, now 404): github.com/analytics-zoo/vll…
Write-up of the PR: iamag.it/en/news/qwen4-27b-a…
llama.cpp "Get ready for qwen4": github.com/ggml-org/llama.cp…
First spotted:
Kalmia tokenizer test: nitter.cf/aitrackerbot/status/21…
Kalmia back on Arena: nitter.cf/ashxhart/status/211465… nitter.cf/TeksEdge/status/211399…
The other new anonymous Arena model, Kalmia, isn't Kimi. It runs on Qwen's tokenizer 🧩
Same chat-box tokenizer test: Kalmia garbled 6 of 8 tokens that Qwen models trip over, and none of the 40 from Kimi, DeepSeek, OpenAI, Google and Mistral. Qwen 3.7 and 3.8 garble the same ones.
That points to an unreleased Alibaba Qwen model, though a few other labs build on Qwen's tokenizer too.
Thomas Eastham retweeted
Feeling really bad for the @Tailscale folks
Lots of people have no idea how it works, that it's more than just Wireguard, the policy and ACL capabilities, that SSH isn't even on by default...
It is wonderful software that someone crapped on, and now everyone wants to roll in it
if you install Tailscale and your impression of it is "wow this is cool!" and not "oh shit, this is just a centralized SSH honeypot?" you're ngmi
Readers added context they thought people might want to know
Tailscale is a peer-to-peer WireGuard mesh VPN; its control server coordinates keys/policies only and sees no traffic. Tailworm report details bugs in optional SSH after prior authorized compromise, not a "honeypot".
tailscale.com/docs/concepts/…
tailworm.com
thehackernews.com/2026/10/unpatc…
tailscale.com/docs/features/…
Thomas Eastham retweeted
Tailscale is great software, so it's unfortunate to see so many people sharing this as "Tailscale got hacked.", which is misleading.
This is not about unauthenticated attackers breaking into random Tailscale nodes. It's what attackers can do after gaining authorized SSH access or compromising an already trusted device.
If I understand correctly, you are secure if you only use Tailscale for your own devices and don't grant anyone else access, which is how many people use it.
Thomas Eastham retweeted
Heads up - don't update your DGX Spark until there's been a bit of time to work out whatever is going on...
Thomas Eastham retweeted
GLM 5.5/OMNI will be a shift from Moore's Law to RSI
(Recursive Self-Improvement) in
Al is officially here.
Key takeaways from Tang Jie (Founder, Zhipu Al) on what's driving the next era of foundation models:
Execution > Chat: Value is moving from conversational Ul to long-horizon task completion.
Scaling Bias: Small-scale experiment conclusions don't linearly translate to trillion-parameter systems.
The RSI Loop: Models creating environments →
evaluating
rewards • training themselves.
Full credit to @NFT_Chen who has been the absolute master of breaking news!!!
🤖 Made with AI