Why create and maintain a semantic layer in your data stack?
It's never been easier to connect agents to your databases. But add more complexity - more users, bigger and messier databases, an existing web of metrics, dashboards, shared understanding - and reliability and governance get a lot harder.
A semantic layer helps you define your key metrics and how agents can leverage and combine them to build reliable data analysis.
Existing semantic layers can be painful to maintain and too rigid for agentic use cases. After struggling with those, the team at Motley built one: SLayer, open-source, maintained with agents and built for agentic interaction.
Test out the hosted version here: https://t.co/52GVfiMWDx
Code is open-source, check it out on GitHub: https://t.co/X6uJ90wb2H
i want to be careful here because this space is full of people overclaiming.
what i tested: a 30 second two-hander. two people, one location, four hard cuts, dialogue generated with the video rather than dubbed on after.
what i checked: whether the same two faces come out the other side of each cut, whether eyelines match across a shot-reverse-shot, whether the voices stay distinct.
all three held. the performance direction i wrote as a sequence of physical beats got performed in that order, which is the part i still find slightly unnerving.
what didn't work: any text in frame. and it doesn't like more than about five people in a shot.
that's it. no grand claim. it's Wan 3.0, i ran it on https://t.co/8ry1e73usa, and it's free to start if you want to check my working.
WAN 3.0 just dropped and I'm still processing what I saw. π€―
Fed it a rough idea and watched it hold a scene together for a full 30 seconds ....consistent characters, consistent lighting, zero glitches between cuts. That's not a small thing in video gen.
And the wildest part? It's on the Pika API Club..... which is less expensive than running it anywhere else. No countdown timer, no fine print. Just the better price.
If you've been waiting for frontier video models to feel accessible, this is the one. https://t.co/ahGFt1Z5E8
Starting today: unlimited concurrency on LiveAvatar.
Run 1 avatar or 10,000 at once. Same API, full-body 1080p, down to $0.01/min at scale.
Here's why we removed the limits π https://t.co/zrteJUm7Gf
This feels much closer to an actual marketing workflow than a typical AI tool. From positioning and pricing to copy, launches, GEO, and analytics, every role contributes to the same loop and continuously improves the final output.
We just open-sourced our entire marketing playbook.
After huge viral success with our Marketing OS running on Grok Bot, we decided to bring it to Claude.
Introducing Marketing AGI by Maxfusion.
You no longer need your first 5 marketing hires.
π Marketing Department
β
β£ π Head of Marketing
β β£ π Positioning & Offer Design
β β£ π Pricing Strategy
β β π Competitor Teardowns, Public Data Only
β
β£ π Copywriter
β β£ π 20 Variants, Panel-Scored
β β£ π Email Sequences, Written in Full
β β π Posts Built to Survive the Feed
β
β£ π Creative Strategist
β β£ π The 18-Tactic Hook Engine
β β£ π Fatigue Caught at the Concept Level
β β π Next Tests, Ranked & Briefed
β
β£ π Launch Lead
β β£ π The Runway Before Launch Day
β β π Product Hunt, Hour by Hour
β
β£ π GEO Lead
β β£ π Ranked by ChatGPT & Perplexity
β β π App Store, Metadata to Screenshots
β
β π Analyst
γ β£ π Audits, Weighted 0-100
γ β£ π Numbers That Mean Something
γ β π AI Slop, Caught on Sight
And they coordinate all the work:
A single Claude skill runs the whole loop:
The Analyst finds the problems, the Copywriter rewrites it, the Creative Strategist tests the new version, and the Analyst grades the outcome.
Comment "AGI" and I'll send you the setup.
$0.01/sec for Seedance 2.0 Fast? π
Thatβs now available through CapCut across Web, App and PC.
But don't look at the price alone.
You also get an easy-to-use platform and access to powerful AI models β all in one place.
Web: https://t.co/QhzMNkxGn7
App: https://t.co/Ian22Kufl2
#CapCut #Seedance25 #CapCutai #CapCutDidThat
Everyone obsesses over the model. Dyna just showed the real unlock: the data pipeline.
1M hours of egocentric video is wild, but the fact they had to rebuild ingestion, manifests, and storage to even train it is the story. Models are easy. Scale is hard.
GLM-5.3 post-trained on a 743B base and it's already topping coding + cyber benchmarks. Open models just got a new standard for agentic + defense work.
The shift: from "AI generates" to "AI produces".
Seedance 2.5 with targeted editing + multimodal refs means you can iterate without starting over. That's production-ready.
This is huge.
ChatGPT Sites are now team workspaces.
Git + CI handled by Codex, we just build.
The era of solo creators is over. Teams + AI wins.
@OpenAIDevs
This is the shift.
Others benchmark what AI knows.
@Apodex_AI benchmarks how AI discovers.
TRACES is measuring the process, not just the answer.
The future of AI eval is here.
This is how RSI actually happens.
Not by tweaking hyperparameters.
By having AI agents improve the training algorithm itself.
@EinsiaAI's AI4AI-Bench testing this on 10 real research repos is the kind of work that moves the field forward.
This is huge.
An mRNA cancer vaccine clearing Phase 3 trials is not just news.
It's the future of medicine arriving.
@TheRundownAI's daily tech rundown always catches the stories that actually matter.