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Welcome, humans.
Humanoid robots have finally reached the most important milestone in civilization: getting clipped by a jump kick for clout:
The robot fight clip that gave Matador the world's least OSHA-approved comeback arc.
The clip is from URKL, the Ultimate Robot Knock-out Legend league hosted by Chinese robotics company EngineAI, and features two humanoid robots trading chaotic kicks, falls, and committed recoveries.
Early on in the clip, the white robot lands a kick that sends its opponent into full we'll fix it in post territory, and commenters immediately reached the correct conclusion: it was a better fight than the McGregor one from last week.
The serious version: the clip is funny because the robots still look like expensive Rock 'Em Sock 'Em boxers in real life. It is also eerie because they get back up fast, keep going after ugly hits, and make “is this autonomous or teleoperated?” feel less like a nerd argument and more like something that reeeaally changes your view of robot progress depending on the answer (to date, we don’t know; if you do, tell us!)
Our favorite part? One robot, apparently named Matador, fought two-thirds of the match with its head hanging on by a wire. UFC fighters could never... thankfully not!!
Here’s what happened in AI today:
😺 Netflix made AI a line item in Hollywood.
📰 Kimi K3 pushed open models toward the frontier.
📰 China pitched itself as the developing world's AI partner.
🍪 V2Fun turned prompts and videos into 3D characters.
🎓 LTX 2.3 works best as a shot list.
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😺 Netflix Just Made AI a Line Item in Hollywood
Hollywood expected AI’s big reveal to be a fully synthetic blockbuster with a suspiciously smooth leading man. Netflix’s version is less dramatic and probably more important: AI is already in the budget spreadsheet.
Here's what happened:
Netflix said roughly 300 titles on its platform used generative AI, mostly during post-production.
The company pointed to The American Experiment, Glory, and Brasil 70 as examples where AI helped create crowd shots, battle scenes, and worldbuilding shots.
Co-CEO Ted Sarandos said The American Experiment included 17 minutes of “AI-enhanced footage” made twice as fast and at half the cost of previous options.
Why this matters: This is entertainment’s first boring AI phase, which is usually the phase that sticks. Netflix is telling investors AI can add shots a production might skip, compress timelines, and make expensive scenes possible for shows without Marvel money.
That shifts the fight from “Can AI make art?” to “Who decides where AI fills the gaps?” If AI-enhanced footage becomes routine post-production, studios can use it before audiences know what changed. Somewhere, a VFX budget just heard boss music.
Our take: Netflix is probably previewing generative AI’s normal path in media: not replacing human production all at once, but making more shots economically possible, then quietly changing what gets greenlit. That helps smaller teams build bigger worlds. It gets messy if “AI-enhanced” becomes a vague label for labor cuts, training-data questions, or creative shortcuts.
Next fight: disclosure. Studios will want flexibility. Creators will want credit and guardrails. Viewers will want to know whether what they loved was handmade, AI-assisted, or both.
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🎓 AI Skill of the Day: Make LTX 2.3 Behave Like a Shot List
LTX 2.3 is the best totally open video model atm, and it can seriously look amazing, but some people still struggle with it. The thread’s advice: don’t ask for a whole movie. Make one strong still, animate a short controlled shot, then stitch shots together.
The LTX 2.3 example behind today's shot-list workflow.
Here’s the recipe for success using this model:
Use ComfyUI’s default LTX workflow.
Pick fp8 if you’re on 12GB VRAM.
Start from clean Krea2/LoRA stills (LoRAs keep style/character consistent; this video explains them).
Keep clips short, and use 18fps at 720p as the proven baseline.
For longer clips:
Feed a reference frame instead of pure text.
Try guidance/“CFG” around 2.5. That tells LTX how tightly to follow your prompt; start there, then adjust.
Inject a second reference near frames 45-50 on an 8-second clip.
Write enough action to fill the full duration.
One commenter warned that a 30-second prompt with only 6 seconds of action becomes filler fast. If “prompt enhance” wrecks motion, turn it off. Then patch rough frames with EbSynth or inpainting and finish with color grading.
Our favorite insight: LTX 2.3 works best when you direct it like a shot list, not a screenplay. Sample prompt:
Build a single shot.
Starting image/reference: [describe or attach the clean still]
Length: [6-8 seconds]
Frame rate/resolution: [18fps, 720p]
Camera: [locked / slow push-in / close-up / high angle]
Identity details: [face, outfit, hair, style]
Action: [one clear movement or beat]
Motion limits: avoid fast camera movement, identity drift, morphing, and filler.
If extending: use the last frame or inject a second reference around frame 45-50.Total AI beginner? Start here (goes with this video).
Have a specific skill you want to learn? Request it here.
🍪 Treats to Try
Zro gives coding agents private open-model inference with zero request retention across multi-region infrastructure —free options, first month $1.
Unabyss for Claude gives Claude workflows shared memory across apps, agents, and LLMs with stale-context conflict resolution —pricing not public.
Forall helps coding agents generate spec-driven code with machine-checkable proofs for TypeScript, Java, and Rust —free/open-source.
Scribe turns git history, Claude Code/Codex sessions, and saved links into a self-updating markdown knowledge base —free/open-source, plus model costs.
Campus / Skills AI gives teams one project space where people and AI agents collaborate on software work —pricing not public.
📰 Around the Horn
A viral r/aivideo clip turned a dachshund ride into a hyperreal AI skate stunt, complete with an impossible flip that feels like Vine but with AI.
Meta and Anthropic reportedly discussed a potential $10B compute-rental deal, turning Meta's giant AI infrastructure buildout into a possible cloud business for rival model labs.
SpaceX reportedly negotiated to provide the Pentagon with billions of dollars of AI data-center capacity, deepening Musk's move from rockets and models into government AI infrastructure.
The U.S. Navy approved an AI-first data strategy that treats slow deployment as riskier than imperfect alignment and aims to run models and agents directly on ships and Marine units.
Chinese President Xi Jinping pitched China as the AI partner for the developing world, warning against security overreach while offering 5,000 AI training opportunities, cooperation with ASEAN, the African Union, the Arab League, and BRICS, and a Shanghai-based World Artificial Intelligence Cooperation Organization with 29 countries.
Apple was ordered by San Francisco City Attorney David Chiu to remove eight “nudify” AI apps after his office accused it of aiding access to nonconsensual sexualized-image tools.
Microsoft apparently prepared Project Perception, an AI security product using Anthropic, OpenAI, and Microsoft models to find and automatically fix software bugs.
Apple reportedly sent preservation letters to around 40 former employees now at OpenAI as its trade-secrets lawsuit against OpenAI and io Products expanded.
Microsoft CEO Satya Nadella supposedly told Copilot engineers Anthropic's Fable restrictions “don't make sense,” calling the model unusually “editorially controlled” despite Microsoft's Anthropic partnership.
ASML offered roughly 44,500 workers €20K in shares if they stay until 2030 because ASML makes EUV machines needed for advanced AI chips.
MLB effectively banned teams from using league-issued dugout iPads for generative AI during games after clubs used custom apps for substitutions, pitch-calling, and in-game decisions.
Patreon started working with Cloudflare to block AI training crawlers, moving creator-platform AI policy from polite robots.txt requests to enforcement.
A UK agency warned cheaper Chinese open models are narrowing the cyber gap with U.S. rivals, giving companies less patch time as open weights get stronger.
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🌟 Sunday Special: The Week's Biggest AI Stories and Tools
Top 5 AI stories of the week:
Kimi K3 showed China's open models nearing frontier quality, with a 3T-class architecture, native vision, and full weights due later this month.
OpenAI's hardware plan became a screenless ChatGPT speaker with cameras, sensors, voice, and movement.
Demis Hassabis pushed for a U.S.-led frontier-AI watchdog as labs, governments, and rivals argued over model testing.
AI infrastructure money stayed enormous, from Databricks' $188B round to Fireworks' $17.5B valuation and the ASML, TSMC, and SK Hynix chip-supply scramble.
China pitched itself as the developing world's AI partner with open-source rhetoric, training programs, standards diplomacy, and a Shanghai cooperation body.
Top 5 AI tools of the week:
Kimi K3, the open 3T-class model that made “open weights near the frontier” feel less theoretical.
Codex Micro, OpenAI's $230 Work Louder keyboard for steering coding agents with physical controls.
V2Fun, the Product Hunt breakout for 3D characters with 8K textures and AI motion capture.
1Password for Claude, a safer way to let Claude sign into sites and use one-time codes.
Forall, the Hacker News standout for formal-verification-flavored coding agents.
A Cat’s Commentary
 | That’s all for now.
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