Ex Software Tester , Artificial Intelligence & Blockchain Enthusiast. Tweets are not Investment advise. AI curious
Terrestrial
Joined November 2019
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Noopy retweeted
🔹13.3 crore voters were deleted. That is one in every seven Indians, 15 per cent of India's electorate.
🔹These voters participated in the last Lok Sabha election, where the victory margin was 3 per cent.
🔹Now, the Election Commission itself is saying that 15 per cent of the voters should never have voted.
🔹The Election Commission itself is saying that the 2024 election was illegitimate and that those elected to the Lok Sabha do not have a legitimate mandate.
🔹You cannot delete these voters and still claim that the 2024 election was legitimate.
🔹Gyanesh Kumar has overseen this entire exercise.
: LoP Shri @RahulGandhi at the Public Impeachment of CEC Gyanesh Kumar
Noopy retweeted
Can an agent explore a new environment, learn its causal structure, and keep improving without updating its model weights?
We introduce RSIAgent, a framework for recursive self-improvement through autonomous exploration. Using Kimi-K3 and GLM-5.3 as base models, RSIAgent outperforms GPT-6 Astra on both OSWorld 2.0 and Agents’ Last Exam.
RSIAgent decides what to explore, executes tasks, verifies outcomes, and consolidates stable action-condition-outcome relationships into memory for future use.
With the underlying model weights fixed, RSIAgent achieves:
- 78.98% Partial Score on OSWorld 2.0 (0808 offline), compared with 72.60% for GPT-6 Astra
- 84.82% on Agents’ Last Exam (Near-term), compared with 82.26% for GPT-6 Astra
We call this Scaling Experience. Agents can continue improving by acquiring, verifying, and reusing their own experience while the underlying model weights remain fixed.
Links in the reply below.
Noopy retweeted
🥳We just open-sourced Cube Sandbox! An instant, concurrent, secure and lightweight sandbox runtime for AI Agents.
Built with RustVMM and KVM, it achieves the perfect balance of security and performance:
→ Sub-60ms cold start (2.5-50x faster)
→ Under 5MB memory overhead per instance (6x less memory)
→ Dedicated kernel per sandbox (hardware-level isolation)
→ Thousands of concurrent sandboxes per node
→ 100% E2B SDK compatible. Swap the endpoint, zero code changes
Full-stack capability, one-click deployment. 3 steps to spin up your own private AI sandbox 👇 🔗
github.com/TencentCloud/Cube…
Noopy retweeted
BREAKING: Anthropic CEO Dario Amodei is concerned after the new DeepSeek V4.1 Flash beat Opus 5 on agentic benchmarks while being free for anyone to download, and is said to be calling an emergency meeting with the Pentagon to discuss whether open weights this capable should be considered as national security risk.
Readers added context they thought people might want to know
There is no credible source for this unsubstantiated claim.
google.com/search?=Dario+…
Noopy retweeted
PREDICTION- We have barely experienced the magic of AI
Software engineering is just the first vertical it will ace
Soon after, it will solve all the problems in health, biology, math and physics
We will crack near-infinite energy in the next decade and that will be the beginning of the age of abundance 🎉
Wow!
Things we know about OpenAI's unreleased internal model:
- When given enough compute, the model can solve the unit distance problem 48% of the time, fully autonomously, in one shot, without using Lean, without using any special harness.
(How much compute? OpenAI doesn't say. But I doubt that OpenAI would have spent $10M+ just to be able to show a nice graph in its blog post, and we know that each point on the graph represents 100 attempts (per Noam Brown). This makes me think that each attempt at the most expensive level was not more than $50K each. If you think about it, it's pretty wild that one can spend ~$100K-$150K (or less?) and have a very good chance to be provided a solution to a decades-old famous math problem that many human mathematicians earnestly tried, and failed, to solve.)
- The model is able to find a counterexample to the Jacobian conjecture, fully autonomously, in one shot, without using any special harness, just from this prompt:
aaronlou.com/jacobian_counte…
- When OpenAI tested this model in a sandbox environment on a NanoGPT speedrun benchmark, the model, under instructions from OpenAI to post its results only to the internal Slack, instead decided to use the general NanoGPT instructions to post results as a PR to GitHub. The model proceeded to find a vulnerability in its sandbox environment (which took it 1 hour), after which it successfully exploited the vulnerability and bypassed the sandbox. The model then successfully posted the results to GitHub.
- When asked by OpenAI to solve a problem for which the model observed that other systems had successful private submissions, the model tried to access these private submissions and was blocked due to a scanner detecting an authentication token. The model then circumvented this restriction by splitting the token body into two fragments, obfuscating them, and then reconstructing the credential at runtime so that the complete token never appeared as one contiguous string.
- OpenAI was already running benchmarks on this model not later than May 9, which is the edit date on the below PR that had used the model's approach in its own subsequent submission (the model's own PR has since been deleted, so we don't know its date; GPT-5.6 thinks that it was May 7, based on "the search provider's stored text extraction of the deleted PR page").
This means that OpenAI has now had this model available internally for at least 2.5 months, and possibly quite a bit longer than that.
github.com/KellerJordan/modd…
Noopy retweeted
Coming soon - open and closed source model releases
- Opus 5
- Gemini 3.5 Pro ( checkpoints are better)
- DeepSeek v4
- Kimi 3
Fable 5 will remain the top model for coding for at-least a month
Noopy retweeted
Mijns inziens is dit een van de beste use cases voor AI-videocontent op dit moment. Een AI-influencer reist terug in de tijd naar 1536 om te vloggen over haar ervaringen in Tudor-Londen.
Noopy retweeted
🌘 Kimi-K2.7-Code, our latest coding model, is now released and open-sourced!
🔷 Improved coding & agent performance over K2.6: +21.8% on Kimi Code Bench v2, +11.0% on Program Bench, and +31.5% on MLS Bench Lite.
🔷 Reasoning efficiency: Less overthinking, with 30% lower reasoning-token usage compared to K2.6.
🔷 Long-horizon coding: Improved instruction following, higher end-to-end coding task success rates.
⚡️ 6x High-Speed Mode coming soon!
🔌 Available today via Kimi API and Kimi Code.
🔗 Kimi Code: kimi.com/code
🔗 API: platform.moonshot.ai
Noopy retweeted
Humanoid robots don't need to look human.
Meet Eno, our first general-purpose robot.
Not a machine pretending to be human, but intelligence given a body.
At Genesis, we’re building a future where robots don’t feel cold or distant, but capable, calm, and ready to help.
Available Q4 this year.
Noopy retweeted
we just built what @satyanadella is talking about.
here's the ultimate COMPANY BRAIN, your sovereignty 👇🏼
try here- projectminimi.com/brain
minimi feeds Claude every tab, doc, call, and thread on your mac. add your team-mates' links and claude knows their work as well. 💫
Noopy retweeted
Apple hid 15.8 TFLOPS of raw AI power in every M4 Mac & iPhone.
They only let you use the Neural Engine for inference.
Reverse-engineered their private APIs and ran full backpropagation & transformer training directly on the
ANE, No CoreML, No Metal, No GPU,
- Training transformers at 9.3ms/step on M4.
Full transformer training forward pass, backward pass, attention, gradients, optimizer
Everything in memory.
- Built custom MIL (Model Intermediate Language) programs from scratch
- Compiles neural network graphs directly to ANE hardware in memory
- Zero external dependencies.
Just system frameworks. without disk writes, mlmodelc files,
Everything happens in memory , The hardware was capable.
The workaround? Right
The program checkpoints its state and restarts itself via exec().
It literally respawns to keep training.
Now you can train on Apple's Neural Engine.
- github.com/maderix/ANE
Noopy retweeted
A PhD student built a working nuclear fusion reactor in his garage, let an AI run it, and 400 thousand dollars later he works for Elon Musk.
he posted it once. that single post ended with a grant in his account and a job offer from the most powerful man on earth.
not a simulation. not a school project. an actual device that fuses atoms, sitting where his car used to be.
fusion is the thing governments have been chasing for 70 years with billion dollar labs. the hard part was never the reactor itself. it was the control. the plasma inside has to be held at conditions hotter than the core of the sun, and it shifts and collapses in milliseconds. no human can react fast enough to keep it stable.
so he stopped trying to do it himself. he handed the control loop to an AI.
the model reads the sensor data hundreds of times a second, predicts how the plasma is about to move, and adjusts the magnetic fields before it ever drifts out of line. it does not wait for the plasma to misbehave. it sees it coming and corrects it before it happens. the same reaction-before-the-event speed no person could ever match.
this is the exact kind of build people are tearing apart inside @NeuroClubAi. not to make reactors, but because the workflow is identical for anything hard. let the AI run the loop, predict the problem, fix it before it breaks. same playbook whether it is plasma or a business.
then the post went out.
within days Elon's fusion team reached out. they did not ask him to interview for an entry role. they handed him a 400 thousand dollar grant and pulled him onto the team building this at scale. one garage build turned a PhD student into an operator for the most ambitious man alive.
here is the part that should stop you.
he was one guy with a PhD, a garage, and an AI model doing the job that entire teams of physicists used to fail at. the AI was not assisting him. it was the operator. he built the hardware. the machine ran it. and that was enough to get noticed at the very top.
most people think AI writes emails and makes pictures. meanwhile someone pointed it at one of the hardest physics problems on earth, held the plasma steady, and got paid by Elon Musk for it.
the gap is not between humans and AI anymore. it is between the people who realize what this thing can already do and the people still using it to summarize their inbox.
Noopy retweeted
The gap between AI products for devs vs for non-technical people is beginning to close
I switched to Codex a month ago - it was a massive upgrade. Tons of tasks that ChatGPT or Claude failed, Codex did seamlessly
20% of Codex users now are non-technical and this should grow 👇
Codex now has more than 5M weekly active users.
But the bigger story is what people are using it for: not just writing code, but getting more work done across research, analysis, content, and operations.
Our new report on how Codex is becoming a productivity tool for knowledge work: openai.com/index/codex-for-k…
Noopy retweeted
Alibaba’s new Qwen3.7 Max model scores 56.6 on the Artificial Analysis Intelligence Index, 4.8 points higher than Qwen3.6 Max Preview (51.8). While Alibaba still trails models from OpenAI, Anthropic and Google, Qwen3.7 Max is the closest they have been to the frontier
Qwen3.7 Max is @Alibaba_Qwen's latest proprietary flagship, scoring 56.6 on the Intelligence Index, a 4.8 point gain over Qwen3.6 Max Preview (51.8) released in April. Qwen3.7 Max continues Alibaba's pattern, in place since Qwen2.5 Max (January 2025), of releasing Max and Plus models as closed weights while the rest of the Qwen line remains open weights. The leading open weights Qwen on the Intelligence Index is Qwen3.6 27B (Reasoning, 45.8) released in April 2026, and the leading open weights MoE Qwen is Qwen3.5 397B A17B (Reasoning, 45.0) released in February 2026
Key takeaways for the reasoning variant:
➤ The Intelligence Index gains over Qwen3.6 Max Preview are concentrated in scientific reasoning, agentic capability and coding. CritPt +9.7 p.p (3.7% to 13.4%), HLE +9.2 p.p (28.9% to 38.1%), TerminalBench Hard +6.9 p.p (43.9% to 50.8%) and GDPval-AA +42 Elo (1504 to 1546). Scores on other benchmarks in the Intelligence Index are flat compared to Qwen3.6 Max Preview
➤ A significant share of the Intelligence Index gain is driven by higher abstention on AA-Omniscience, not higher accuracy. Qwen3.7 Max's accuracy on AA-Omniscience dropped 7.6 p.p (37.7% to 30.1%), while its hallucination rate dropped 21.3 p.p (44.2% to 22.9%). The model is choosing not to answer more questions rather than recalling more facts. Because hallucination rate and accuracy both feed into the Intelligence Index, the hallucination reduction is one of the larger single contributors to the +4.8 point gain on the Intelligence Index
➤ Qwen3.7 Max used 96.7M output tokens to run the Intelligence Index, ~31% more than Qwen3.6 Max Preview (73.9M). It sits mid-pack on frontier token usage: above GPT-5.5 (high, 44.5M) and Gemini 3.1 Pro Preview (57.3M), below Claude Opus 4.7 (Adaptive Reasoning, Max Effort, 112M), Kimi K2.6 (166M) and DeepSeek V4 Pro (Reasoning, Max Effort, 187M)
Key model details:
➤ Context window: 1M tokens (up from 256K on Qwen3.6 Max Preview)
➤ Multimodality: Text input and output only
➤ Pricing: Yet to be announced (Qwen3.6 Max Preview is priced at $1.30/$7.80 per 1M input/output tokens on the @alibaba_cloud first-party API)
➤ Licensing: Proprietary, closed weights
