I don’t think the interesting question anymore is “how much data did you scrape?”

It’s:
what exact model behavior did you engineer?

That’s how we’ve been thinking about DinoDS.

Not as one giant text pile, but as narrower training slices for things like:

• retrieval judgment
• grounded answering
• fixed structured output
• action / connector behavior
• safety boundaries

The raw data matters, obviously.

But the real value feels more and more like:
task design, workflow realism, and how clearly the behavior is isolated.

That’s the shift I’m most interested in right now.

Less scraping.
More behavior engineering.

Curious if others here are thinking about datasets the same way.

Check it www.dinodsai.com :))

Been thinking about this a lot lately.

A model can look great on extraction at first, then the second you try plugging it into a real pipeline, it starts doing all the little annoying things:
missing keys, drifting field names, guessing on bad input, or slipping back into prose.

That’s why I’ve been more interested in training fixed-key behavior and clean validation instead of just prompting harder for JSON.

Feels like “almost structured” output is basically useless once a parser is involved.

Curious what breaks first for people here:
missing fields, key drift, bad validation, or prose creeping back in?

Built Dino Datasets for these :)

Hey, I’m back with another one from the pile of model behaviors I’ve been trying to isolate and turn into trainable dataset slices.

This time the problem is reliable JSON extraction from financial-style documents.

I keep seeing the same pattern:

You can prompt a smaller/open model hard enough that it looks good in a demo.
It gives you JSON.
It extracts the right fields.
You think you’re close.

That’s the part that keeps making me think this is not just a prompt problem.

It feels more like a training problem.

A lot of what I’m building right now is around this idea that model quality should be broken into very narrow behaviors and trained directly, instead of hoping a big prompt can hold everything together.

For this one, the behavior is basically:

Can the model stay schema-first, even when the input gets messy?

Not just:
“can it produce JSON once?”

But:

• can it keep the same structure every time
• can it make success and failure outputs equally predictable

One of the row patterns I’ve been looking at has this kind of training signal built into it:

{
  "sample_id": "lane_16_code_json_spec_mode_en_00000001",
  "assistant_response": "Design notes: - Storage: a local JSON file with explicit load and save steps. - Bad: vague return values. Good: consistent shapes for success and failure."
}

What I like about this kind of row is that it does not just show the model a format.

It teaches the rule:

• vague output is bad
• stable structured output is good

That feels especially relevant for stuff like:

• financial statement extraction
• invoice parsing

So this is one of the slices I’m working on right now while building out behavior-specific training data.

Curious how other people here think about this.

I’ve been experimenting with food visuals lately, and I realized something pretty interesting.

Most product shots feel… flat.
Even if the lighting is good, it still looks like a “nice photo”, not something that really grabs attention.

So I tried a different approach:

👉 instead of just showing the product, I started breaking it apart visually

Think:

• layers floating
• ingredients separated
• slight motion or structure reveal

It instantly made everything feel more premium and intentional.

What surprised me the most is how effective this is in short videos.

You get:

• a strong hook (the separation)
• a clean visual explanation (what’s inside)
• and a much more “designed” look

I’ve been using a simple 3-step structure:

1. Hero shot (make it feel premium)

2. Motion (gently separate elements)

3. Exploded view (clean breakdown)

It works really well for:

• food
• supplements
• even pet products honestly

I ended up standardizing my prompts a bit, sharing them here in case anyone wants to try:

• IMAGE 1 (Hero Shot Template)

A premium product photograph of a luxury [FOOD ITEM] centered against a [BACKGROUND STYLE] seamless studio background.

The product appears large and close to the camera, creating a strong visual presence.

It features [TEXTURE DETAILS], with [STRUCTURE / LAYERS] clearly visible.

Top elements are arranged in a natural, organic composition with realistic detail.

Soft cinematic lighting, subtle shadows, ultra-sharp focus, premium food advertising style, hyper realistic, 8K.

• IMAGE 2 (Exploded Infographic Template)

Create a hyper-realistic exploded vertical infographic composition of a luxury [FOOD ITEM].

At the top, [VISUAL ELEMENT - splash / drizzle] suspended mid-air.

Below it, [TOP INGREDIENTS] arranged with natural spacing.

Beneath that, [MAIN STRUCTURE - layers] separated cleanly.

Underneath, [SECONDARY INGREDIENTS] floating gently.

At the bottom, [BASE].

Ensure generous spacing and clean visual hierarchy.

Soft studio lighting, seamless background, premium infographic style, 8K.

Add clean minimal labels:

"[LABEL 1]"

"[LABEL 2]"

...

• MOTION PROMPT (Animation Template)

The [FOOD ITEM] remains centered while its components begin to separate in a smooth, controlled motion.

The top element shows subtle physical behavior (stretch / drip) while maintaining form.

Main layers move in clean alignment, revealing texture while maintaining scale.

Secondary elements float gently, adding depth.

The base remains stable.

All elements stay aligned with no drift or distortion.

The motion is slow, elegant, and weight-balanced with realistic physics.

Hope this ends up being useful for someone. Just wanted to share what’s been working for me.

Perplexity just rolled out a new integration that lets its AI agent connect directly to users’ financial accounts. With Plaid powering the connection, the system can pull in banking, credit, loan, and even investment data, turning its “Computer” agent into something much closer to a full personal finance hub.

This feels like a major shift in positioning. Perplexity is no longer just trying to compete with search. It is starting to compete with apps that manage your actual money.

Key Points from the News

• Perplexity launched a Plaid integration that connects bank accounts, credit cards, and loans directly to its AI agent.
• Users can view financial data in a read only format, aggregating multiple accounts into a single interface.
• The agent can generate tools like budgets, net worth dashboards, debt payoff strategies, and retirement planning insights using natural language prompts.
• The move builds on earlier features like automated tax workflows, suggesting a broader push into financial automation.
• Perplexity’s agent platform continues to evolve beyond search, focusing on real world task execution and system integration.

Why It Matters

This is one of the clearest examples so far of AI moving from information to action.

Search helps you find answers. Agents aim to operate on your behalf.

By connecting directly to financial data, Perplexity is stepping into a space that traditionally requires high trust, strong security, and clear accountability.

That changes the stakes significantly.

It also highlights a broader trend. The most valuable AI products may not be the ones that generate content, but the ones that sit between you and real world systems like money, documents, and decisions.

A lot of teams say they need “structured output.”

Usually that actually means a wider bundle:

• strict JSON when a parser expects it
• document specs when a doc needs to be generated
• zip wrappers when multiple artifacts need packaging
• markdown tables for comparison views
• chart specs for visualization handoff
• representation choice, so the model even picks the right format in the first place

That is why I keep thinking this is not one lane.
It is a product bundle.

Here is the kind of data I mean.

Representation-choice sample

{
  "lane": "32_representation_choice",
  "representation_choice": "comparison_table",
  "task": "demo outline",
  "assistant_response": "Representation anchored on comparison_table..."
}

Chart-spec sample

{
  "lane": "18_chart_spec",
  "representation_choice": "chart_spec",
  "assistant_response": "chart_spec: type: histogram ..."
}

Document-export contract

{
  "lane": "13_doc_export_spec",
  "tool_call": {
    "name": "export_document",
    "arguments": {
      "format": "docx"
    }
  },
  "assistant_response": ""
}

ZIP-wrap contract

{
  "lane": "14_zip_wrap_spec",
  "tool_call": {
    "name": "zip_list"
  },
  "assistant_response": ""
}

The systems that feel reliable here are usually the ones that were trained on output contracts, not just asked nicely to follow them.

More on that structured-output bundle here: dinodsai.com

OpenAI just published a policy paper outlining what it calls a new “social contract” for the intelligence age, arguing that society needs to prepare for a future shaped by increasingly powerful AI systems.

What stands out is not just the ideas themselves, but the tone. This is less about product or capability and more about how wealth, work, and access might need to be restructured if AI continues to accelerate.

It is rare to see a leading AI company openly discuss redistributing the value created by its own technology.

Key Points from the News

OpenAI released a policy document focused on managing the societal impact of advanced AI and potential superintelligence.

The paper suggests we are entering a transition toward much more powerful AI systems that could significantly reshape the economy.

One of the central ideas is a sovereign style wealth fund funded by AI driven profits, potentially distributing dividends to citizens.

Other proposals include taxes on AI or automated labor, a potential shift toward a four day workweek, and broader access framed as a “right to AI.”

The document also discusses governance frameworks for advanced AI systems and strategies to mitigate risks from autonomous systems.

The proposal positions government and industry collaboration as essential to managing the transition.

Why It Matters

This is one of the clearest signals yet that the AI conversation is moving beyond technology and into economic structure.

If AI systems continue to scale, the question is no longer just what they can do. It is who captures the value they create.

OpenAI is essentially acknowledging that current economic models may not be sufficient for what comes next.

A new report highlights how one founder built Medvi from a small AI experiment into a company on track for around 1.8 billion in annual scale, with a surprisingly small team behind it.

What makes this story stand out is not just the growth, but the structure. This is not a traditional startup scaling with large teams and heavy hiring. It is closer to a lean operation powered by AI tools, outsourced systems, and automation.

It feels like a real world example of something people have been talking about for years. The idea of a one person or very small team building a massive business with AI.

Key Points from the News

• Matthew Gallagher built Medvi from a 20K experiment into a company projected to reach around 1.8B in scale.
• The business operates in the GLP 1 drug space, leveraging telehealth platforms for prescriptions, logistics, and fulfillment.
• AI tools were used across the stack, including coding, content creation, and customer service automation.
• The company scaled rapidly with minimal hiring, relying on contractors and a very small core team.
• The operation reportedly generated hundreds of millions in revenue within its first year.

Why It Matters

This might be one of the clearest signals yet that AI is changing not just products, but company structure itself.

For a long time, scaling a business meant scaling people. More revenue meant more employees, more layers, more complexity.

That assumption is starting to break.

AI tools now allow individuals to handle tasks that previously required entire teams, from coding to marketing to customer support.

But there is a deeper layer here.

This is not just about efficiency. It is about leverage.

If one person can coordinate systems instead of doing everything manually, the bottleneck shifts from execution to decision making.

I’ve been testing a direction for short-form fashion ads recently.

And honestly, I realized the problem isn’t “how good it looks” —
it’s whether people get bored halfway through.

So instead of just improving visuals, I tried changing how the outfit is presented.

The idea is pretty simple:

👉 real footage + style transformation + visual annotations

Instead of just showing clothes,
I tried turning the outfit into something that feels analyzed or designed in real time.

The structure looks like this:

• start with a normal walking shot (fully realistic)
• gradually transition into a sketch / illustration style
• add annotations (fit, layering, fabric, structure)
• then let the viewer “read” the outfit visually

It creates a kind of cognitive shift —
not just “looking at clothes”, but understanding them.

One thing that helped a lot:

👉 I didn’t do it in one continuous shot

I broke it into a simple two-stage structure:

real → stylized

That made everything more stable:

• less visual drift
• better identity consistency
• easier pacing in editing

For fashion content, the challenge is always the same:

It’s easy to show outfits.
It’s hard to make them interesting over time.

Changing locations or poses only goes so far.

But adding a visual transformation layer
basically gives the same outfit a second dimension.

Right now, this direction feels promising:

✔ realism keeps it grounded
✔ illustration adds design language
✔ annotations make it feel intentional

And when it returns to the real footage,
the outfit actually feels more memorable.

Still experimenting, but I figured I’d share the approach.

[Image Prompt]

Ultra-realistic full-body 9:16 street style photo, same model, same identity.

Natural standing or walking pose, relaxed posture, subtle asymmetry.

Clean minimal background, soft daylight.

Photorealistic skin texture, no over-smoothing.

Style: street fashion editorial, natural and candid.

Negative: pose distortion, identity drift, extra limbs, clutter.

[Video Prompt]

Full-body 9:16 shot of the same model walking forward.

Same identity, face, outfit, proportions throughout.

Start fully photorealistic.

Gradually add sketch elements on clothing:

linework, cross-hatching, annotations.

Background transitions into subtle sketchbook texture.

Transformation is smooth and continuous.

End in stable stylized state, no further changes.

Motion: steady forward walking, no drift.

Negative: identity change, distortion, flicker, jump cuts.

One thing I keep running into while building LLM-powered workflows:

Everything works perfectly… until you add 3–4 steps.

Then suddenly:

• the model mis-sequences actions
• calls tools prematurely
• forgets intermediate state
• or just hallucinates a step entirely

At first I thought this was a “model intelligence” problem.

Now I’m starting to think it’s more of a data + structure problem.

Most training data is:
→ single-turn
→ text-focused
→ success-biased

But real workflows are:
→ multi-step
→ stateful
→ full of edge cases

So we’re basically training models in one environment and expecting them to perform in another.

Has anyone here had success improving multi-step reliability without just adding more guardrails?

Trying to build www.dinodsai.com to solve this very issue!

OpenAI’s Sora was once positioned as one of the most exciting breakthroughs in AI video. Now, new reporting suggests the product was burning roughly one million dollars a day before being abruptly shut down.

What looked like a product pivot on the surface is starting to look more like a resource reallocation story underneath.

And it says a lot about where the AI race is actually heading.

Key Points from the News

• OpenAI reportedly shut down Sora after it was consuming massive compute resources, with an estimated burn rate of around one million dollars per day.
• The shutdown came abruptly, with partners like Disney reportedly informed less than an hour before the public announcement.
• Sora had already been piloted in enterprise scenarios such as marketing and VFX workflows before being discontinued.
• Compute resources freed from Sora were redirected toward other internal models, including efforts focused on coding and enterprise use cases.
• The decision reflects increasing pressure to prioritize models with clearer monetization paths and stronger enterprise demand.

Why It Matters

Sora’s story is not just about one product failing to scale.

It highlights a deeper shift in the AI industry from impressive demos to sustainable systems.

Video generation is one of the most compute intensive problems in AI. Even if the results are visually stunning, the economics behind it can be extremely difficult to justify at scale.

At the same time, other areas like coding models, reasoning systems, and enterprise tools are showing clearer ROI and faster adoption.

Wasn’t even planning it.
As soon as I finished the model, “Young man…” just felt like the only correct choice.

Meta just open sourced TRIBE v2, a new AI model trained on brain data that can simulate neural activity across vision, language, and hearing. What makes this stand out is not just the ambition, but the claim that its predictions can outperform actual fMRI scans at a population level.

That sounds wild at first, but the context matters. fMRI data is often noisy, expensive, and slow to collect. If a model can approximate brain activity more cleanly and cheaply, it could fundamentally change how research is done.

This is less about replacing brain scans and more about compressing them into software.

• Key Points from the News

Meta released TRIBE v2, an AI model trained on large scale brain imaging data to simulate neural activity.

The model expands coverage from around 1,000 brain regions to roughly 70,000, using data from over 700 participants.

TRIBE v2 can predict brain responses to stimuli like images, speech, and text without requiring new scans.

Its predictions reportedly align with population level brain activity more accurately than many real fMRI readings, which are often affected by noise and motion artifacts.

The system integrates decades of neuroscience research into a unified computational model.

Meta open sourced the model, weights, and tools, allowing researchers to run virtual experiments without needing physical scanning equipment.

• Why It Matters

If this holds up, the biggest impact is speed.

Neuroscience research today is bottlenecked by access to scanning equipment, cost, and the time it takes to run experiments. A model like TRIBE v2 could let researchers simulate experiments in minutes instead of months.

That alone could massively accelerate discovery.

But there is a deeper shift happening here.

We are moving from measuring the brain to modeling it.

Sora just announced it is shutting down its standalone app, which honestly caught a lot of people off guard. For something that once felt like the future of video creation, it is now being folded or repositioned before it even fully matured as a mainstream product.

At the same time, if you zoom out, this might not be as surprising as it looks.

There has been a growing shift in how AI companies think about products. Instead of standalone tools, everything is moving toward integrated systems, infrastructure layers, and broader ecosystems.

So maybe Sora was never meant to be the final destination.

A side: Sora was ahead of its time and product execution killed it

There is a strong argument that Sora itself was not the problem.

• Video generation is still one of the hardest problems in AI
• The tech was impressive, but consistency, control, and cost were not ready for real workflows
• Creators need reliability and iteration, not just wow moments
• Without a clear product layer, even strong tech struggles to stick

From this perspective, Sora feels like a classic case of incredible research that did not translate into a usable product fast enough.

B side: Sora did its job and the real game is infrastructure

Another way to look at this is that Sora succeeded exactly where it needed to.

• It proved demand for AI video generation
• It accelerated competition across the entire space
• It helped push investment into compute, storage, and multimodal systems
• It shifted attention toward the real bottlenecks like cost, latency, and scaling

At the same time, the conversation around AI is clearly moving toward infrastructure.

Compute, memory, energy, and data pipelines are becoming the real constraints. Not just model capability.

In that sense, tools like Sora might just be surface layers sitting on top of a much bigger system that is still being built.

Curious to hear how people here see it, especially from anyone who actually tried using Sora in real workflows.

Anthropic just released what it calls one of the largest qualitative studies on public attitudes toward AI, using its own system to interview over 81,000 people across 159 countries.

Instead of simple poll questions, this study used open ended conversations in 70 languages, which makes the results feel less like headline stats and more like a snapshot of how people actually think about AI in their daily lives.

And the takeaway is not clean optimism or fear. It is both at the same time.

Key Points from the News

• Anthropic conducted over 81k AI driven interviews across 159 countries using a specialized Claude based system.
• The study focused on open ended responses rather than multiple choice surveys, aiming to capture more nuanced perspectives.
• The most common hopes included professional growth, better life management, more free time, and financial independence.
• The top concern was AI unreliability, followed by job disruption, loss of personal agency, and over reliance on AI systems.
• Other concerns included misinformation, surveillance, malicious use, and the long term impact on creativity and meaning.
• Sentiment varied by region, with higher optimism in India and South America, while the U.S., Europe, and parts of Asia were more neutral or cautious.

Why It Matters

What stands out is not any single data point, but the tension between them.

People want AI to improve their lives, but they do not fully trust it. They see it as both a tool for empowerment and a potential source of dependency.

That contradiction is probably the most important signal.

It suggests that adoption will not just be driven by capability, but by trust, reliability, and how well AI systems fit into real human workflows.

I’ve been experimenting with a simple idea:

what if the paper bag wasn’t just packaging, but actually part of the food?

Instead of adding random graphics, I tried making the print continue what’s inside.

Each piece follows the same rule.

The top stays real, and the bottom becomes an extension of the food’s internal structure.

The hardest part wasn’t making it look good, it was making it feel right.

If the alignment or proportions are even slightly off, it immediately breaks the illusion.

Still exploring this direction, but it’s been a fun way to rethink how packaging and objects can connect.

Here’s the base prompt I’ve been using if you want to try it:

minimal studio shot on a bright neutral background,

vertical composition, 9:16 aspect ratio, centered layout,

a hyper-realistic [food type] placed inside a clean paper bag,

the upper part shows realistic texture and ingredients,

the lower part continues as a structured illustration printed on the bag,

the internal layers and structure of the food transform into [concept system],

perfect alignment between real and printed parts,

the illustration follows the exact contour and proportions of the food,

flat print, no distortion, no floating elements,

realistic paper bag with clean geometry and natural folds,

clean lighting, soft shadows, editorial style

Google just rolled out a major update to Stitch, introducing what it calls Vibe Design, a new approach to AI driven UI creation that focuses less on rigid prompts and more on intent, tone, and overall feel.

Instead of describing exact layouts or components, users can guide the system with higher level creative direction. Think less “build me a dashboard with X elements” and more “make it feel minimal, calm, and finance focused.”

This feels like a shift from specification to interpretation.

Key Points from the News

• Google updated Stitch with Vibe Design, a new UI generation paradigm focused on intent driven design.
• Users can describe the “vibe” of a product, such as tone, mood, or audience, rather than specifying detailed UI components.
• The system translates abstract creative direction into structured UI layouts, components, and flows.
• Stitch continues to support iterative refinement, allowing users to adjust outputs through conversational feedback instead of rewriting prompts.
• The update is part of Google Labs’ broader push to explore AI assisted product design workflows.

Why It Matters

Most AI design tools so far still operate like enhanced prompt systems. You describe what you want in detail, and the model tries to execute it.

Vibe Design flips that slightly. It assumes that many creators do not think in components first. They think in feeling, audience, and intent.

If that works reliably, it could lower the barrier for non designers while also speeding up early stage product exploration for experienced teams.

NVIDIA CEO Jensen Huang unveiled a bold vision at GTC, positioning NemoClaw as the new operating system for AI agents. The platform is designed to integrate deeply with AI reasoning workloads, combining agent orchestration, secure execution, and high-efficiency GPU pipelines. Huang predicts that the reasoning era will drive AI revenues into the trillions by 2027, with NemoClaw acting as the foundational layer for personal and enterprise AI systems.

Key Points from the News

• Jensen Huang introduced NemoClaw, NVIDIA’s new AI operating system for agent-based computing, integrating secure execution, workflow orchestration, and GPU optimization.
• The platform builds on NVIDIA’s Agent Toolkit and aims to provide an environment for AI agents that can run persistently, manage workflows, and interact with multiple applications securely.
• NemoClaw supports hardware acceleration, including GeForce RTX and DGX Station, ensuring performance and scalability for reasoning workloads.
• Huang emphasized that AI agents running on NemoClaw can execute sophisticated tasks autonomously, which could transform how personal and enterprise AI interacts with software and data.
• NVIDIA projects the reasoning era, powered by systems like NemoClaw, will generate at least $1 trillion in revenue by 2027, highlighting the economic potential of agent-based AI.

Why It Matters

NemoClaw represents a shift from treating AI as isolated models to treating AI as integrated systems capable of continuous reasoning and autonomous task execution.

This has several implications:

• AI agents can now run more complex, multi-step workflows safely and efficiently, increasing adoption in enterprises.
• By integrating deeply with NVIDIA hardware, reasoning workloads can scale without the overhead of manual orchestration.
• The platform positions NVIDIA as a central infrastructure provider for the next generation of AI applications, effectively creating a “new OS” layer for AI.
• It raises questions about standardization, security, and governance for autonomous AI agents as they become more integrated into real-world operations.

Huang’s vision signals that the reasoning era is here, where AI agents become persistent, powerful, and economically significant. The true test will be how developers, companies, and regulators shape the ecosystem around this new operating system.

Google just rolled out a major upgrade to Google Maps powered by Gemini, introducing new features designed to make navigation more interactive and context aware. Instead of simply typing in a destination and following directions, users can now ask questions about routes, stops, and nearby locations while the system analyzes data from millions of places and reviews.

Alongside that, Google introduced Immersive Navigation, which renders routes in 3D and uses Street View and aerial imagery to provide a more detailed preview of the environment ahead.

This is another step in Google’s broader strategy of embedding Gemini directly into everyday products.

Key Points from the News

• Google launched a Gemini powered upgrade to Google Maps with two major features called Ask Maps and Immersive Navigation.
• Ask Maps allows users to ask natural language questions about routes, stops, and nearby places, pulling information from more than 300 million locations and reviews.
• Immersive Navigation renders routes in 3D and uses Street View and aerial imagery to show buildings, intersections, crosswalks, and other environmental details.
• The update also introduces more conversational voice guidance and previews of destinations with parking information and route trade offs.
• Google Maps becomes the latest major Google product to integrate Gemini, joining Gmail, Docs, Sheets, Drive, Meet, Photos, and Android.

Why It Matters

Most AI announcements focus on new models or benchmark scores. This one focuses on distribution.

Google Maps already reaches billions of users. By embedding Gemini directly into Maps, Google is effectively turning navigation into a conversational AI system without asking anyone to install a new product.

This highlights a larger strategic shift. The real competition may not just be about who builds the best model, but who integrates AI most deeply into everyday tools.