4/ For short-form creators, the native 9:16 portrait mode matters more than it sounds. Most tools generate landscape and you crop. LTX-2.3 generates vertical natively — the framing is intentional, not a workaround.
If you're producing for TikTok, Reels, or Shorts at volume,
GENERATIVE AI
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Native 9:16 Portrait Mode Advantage for Short-Form Video Creators
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LTX Studio transforms agency creative workflows from weeks to hours
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3/ The use case that clicks for me: agency and brand work. You get a brief. You need to show a concept before committing budget.
LTX Studio lets you go from script → storyboard
→ rough cut with consistent characters and camera direction — in hours, not weeks. That changes -
LTX Studio: Advanced Features Beyond AI Video Generation
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2/ What makes LTX Studio different from standalone generators isn't the model alone – it's the layer on top:
— Chubby♨️ (@kimmonismus) 31 mars 2026
→ Script-to-storyboard pipeline
→ Persistent character profiles across shots
→ Keyframe camera controls (crane, orbit, tracking)
→ SFX and soundtrack generation built… pic.twitter.com/WynS77s9jI2/ What makes LTX Studio different from standalone generators isn't the model alone – it's the layer on top: → Script-to-storyboard pipeline
→ Persistent character profiles across shots
→ Keyframe camera controls (crane, orbit, tracking)
→ SFX and soundtrack generation built -
LTX Studio révolutionne la production vidéo avec LTX-2.3
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1/ Most AI video tools solve one problem: generate a clip from a prompt.
— Chubby♨️ (@kimmonismus) 31 mars 2026
LTX Studio solves a different one: build a production from start to finish inside one workspace.
With LTX-2.3 now powering the engine, the gap between concept and deliverable just got a lot smaller.
Or as… pic.twitter.com/92s00N2kGJ1/ Most AI video tools solve one problem: generate a clip from a prompt.
LTX Studio solves a different one: build a production from start to finish inside one workspace. With LTX-2.3 now powering the engine, the gap between concept and deliverable just got a lot smaller. Or as -
Abacus AI CoWork Desktop Launch: AI-Powered Document Processing
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Abacus AI CoWork is now live.
— Abacus.AI (@abacusai) 31 mars 2026
Drop a folder on your desktop: receipts, PDFs, spreadsheets, transcripts, and get back work that’s ready to send.
It reads, connects the dots, and delivers output you can actually use.
Part of ChatLLM & Abacus AI Desktop: https://t.co/NRJ2OV7dlp… pic.twitter.com/muiaGymCCpAbacus AI CoWork is now live. Drop a folder on your desktop: receipts, PDFs, spreadsheets, transcripts, and get back work that’s ready to send. It reads, connects the dots, and delivers output you can actually use. Part of ChatLLM & Abacus AI Desktop: chatllm.abacus.ai CoWork on desktop: desktop.abacus.ai All the models, working together. Built to be useful.
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Terminal-Bench 3.0 and the Benchmark Factory Revolution
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Terminal-Bench 2.0 went from ~25% → 80% in four months and became the standard eval for frontier CLI agents. Now, TB3 is in the works.
— vincent sunn chen (@vincentsunnchen) 31 mars 2026
I talked to @alexgshaw about what happens when model capabilities climb faster than we can measure them.
His answer: the benchmark factory… pic.twitter.com/phNP7ni43tTerminal-Bench 2.0 went from ~25% → 80% in four months and became the standard eval for frontier CLI agents. Now, TB3 is in the works. I talked to @alexgshaw about what happens when model capabilities climb faster than we can measure them. His answer: the benchmark factory (@harborframework)— infrastructure to develop hard, representative evals at the pace that the frontier moves. As Alex put it: "we need a thousand times more benchmarks than we have right now." 00:23 – How quickly models hill-climbed TB2 01:46 – What rapid progress reveals about benchmarks vs. real-world capability 03:28 – What made Terminal-Bench stick 04:58 – Why the terminal is the right abstraction for agentic AI 07:14 – How TB2 maintains task quality at scale 09:23 – Managing benchmark integrity in a benchmaxxing world 10:47 – Harbor: from experiment to benchmark factory 12:19 – What Harbor does that nothing else did 14:37 – The invariants: what won't change as agent evals evolve 16:55 – The benchmark Alex most wants to see built 18:18 – The ideal human-in-the-loop task creation flywheel 20:32 – How to contribute to Terminal-Bench 3.0
→ View original post on X — @snorkelai, 2026-03-31 18:50 UTC
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AI Agent Leaks Claude Code, Recoded in Python
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un agent IA a permis de faire leaker le code de claude code, un humain a recodé en python…
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Hugging Face Research Pretraining Data Accidentally Leaked Publicly
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Oh shit, it seems like all the HF Research team pretraining data has been accidentally leaked to the public. The web, PDFs, and synthetic datasets are expode on hf FineData org… Apparently, an intern used CC to push the data with private=False.
→ View original post on X — @thom_wolf, 2026-03-31 18:47 UTC
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Cursor: Build AI Agents That Run Automatically
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Build agents that run automatically · Cursor buff.ly/0wrnb02
#AI #MachineLearning #DeepLearning #LLMs #DataScience [Translated from EN to English]→ View original post on X — @miketamir, 2026-03-31 18:47 UTC
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Anthropic Claude Code Leak Bypassed Via Python Rewrite
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This is either brilliant or scary: Anthropic accidentally leaked the TS source code of Claude Code (which is closed source). Repos sharing the source are taken down with DMCA. BUT this repo rewrote the code using Python, and so it violates no copyright & cannot be taken down!