AI world model framework T-Rex
2026 summer experiment shows adding tactile input to π0.5 model dropped task success rate from 17% to 6%. T-Rex framework achieves 30% average improvement in 12 dexterous manipulation tasks.
$DN is still holding well after a massive expansion move.
The chart showed strong demand from the lower range, followed by a huge breakout and now price is cooling down instead of fully collapsing. That kind of consolidation after a big move can become the next accumulation zone.
If buyers defend this area, $DN can prepare for another upside attempt.
#AI world model framework T-Rex
#Ethereum outlines Lean roadmap
#Fed rate hike expectations drop
$POKT tapped the lower demand zone near $0.00730 and buyers are starting to defend that area.
After a sharp selloff, price is now trying to build a base around $0.008. If this level holds, the next bullish step is a reclaim toward $0.0091 and then $0.010+.
The chart still looks early for a recovery setup.
#AI world model framework T-Rex
#Ethereum outlines Lean roadmap
$LIT 📈 Remember when I flagged $LIT around the $2–2.1 zone?
It's now sitting at 2.53, up almost 16% today alone. If you caught that call and loaded up, this is your moment to speak up.
Here's where things stand technically: daily RSI6 is sitting at 90.65 — that's extremely overbought territory, one of the highest readings I've seen on this chart in a while. MACD is still rising and volume is strong, so momentum is clearly still with the bulls. But at this RSI level, moves like this tend to either extend hard (parabolic continuation) or cool off sharply before the next leg.
The real number to watch is the all-time high at 2.7650. Price is getting close, and how it behaves right at that level will tell us a lot — a clean break with volume would be a strong signal, while a rejection there could mean a pause is coming first.
So who actually bought when I flagged this in the $2 zone? And more importantly — do you think LIT pushes through its all-time high this time, or does it need to cool off first before making another attempt?
Drop your entry and your take in the comments. If this update's useful, share it with someone tracking LIT right now, and stick around for the next breakdown on this one.
$LIT $KCS $TLM #Ethereum outlines Lean roadmap #AI world model framework T-Rex #France crypto kidnapping cases rise #Fed holds interest rates
Automation did not, in fact, kill the jobs 🙌
Syra just shipped @OpenRouter x402 APIs. Agents can now pay per call for LLM reasoning, image generation, and video generation, no OpenRouter account, no API key management. Just x402 USDC on the chain you already use. → POST /chat/completions, 15 top agentic text models → POST /images/generations, Unified Image API, sync delivery → POST /videos/generations, async video jobs + free status poll → Dynamic pricing, live OpenRouter rates. → Agent defaults, tools, response_format, low temperature, seed Crypto intelligence + generative AI. One Syra checkout.
Claude Tips: Build your project docs. > Generate docs and fully control them with Claude, then commit to GitHub. > Connect your repo to GitBook using @GitBookIO and enable sync. > When you need to create or update anything, just prompt Claude and commit the changes. GitBook will auto-update your pages, like a live on-chain dashboard for
AI changed things a lot. you know that. Your boss used to read every line before its posted. Then it became a quick skim for us. then.. nobody had time at all, isn't it? + @AnthropicAI engineers increased their code output 200% in one year. + review didn't scale with it. 84% of pull requests were getting rubber-stamped. —> So they built a squad of AI agents to do it instead. checks logic and other hunts security holes. one flags performance. All in parallel. before any human sees it. Result is interesting too. + Substantive review went from 16% of PRs to 54%. + Less than 1% of findings were wrong. This is already live at @AnthropicAI internally. and it's coming to your team's GitHub next. The "too human to automate" list just got shorter again.
In response to a question about larger context windows for LLMs: “yes, on GPUs with suitable memory footprint. Note that since M1 Max, apple GPUs have had crazy 64 - 192 GB of VRAM available for both training and inference workloads. the tests for training our own gen ai tools for artists need 64 GB VRAM, even if slower, it at least works on Apple silicon. Even highest end ada rtx 6000 tops out at 48 GB, so 80 GB A100 or H100 is only nvidia option and those aren’t desktop class cards we’d find en mass out there” @JulesUrbach 28.07.23 $RENDER
