NVIDIA Introduces RTX Spark

In partnership with

NVIDIA unveiled RTX Spark: AI Is Moving From the Cloud to Your Laptop (source)

It’s a new AI-focused chip designed to run advanced AI workloads directly on personal computers. The announcement signals a major shift toward local AI processing instead of relying entirely on cloud infrastructure. Hardware partners including Dell, HP, Lenovo, ASUS, Acer, and Microsoft are expected to adopt the platform. If successful, AI agents capable of working independently on consumer devices could become far more common over the next year.

How You Can Make AI 460x Faster Than RAG (and Free to Run)

We killed RAG and sandboxes. Here's what we built instead.

Our documentation assistant had a problem. RAG pipelines only sent the model page fragments, making responses feel half-baked. The fix was obvious: give the agent full doc access. Quality jumped, but startup hit 46 seconds and costs ballooned to $70K+/year.

The insight: the agent doesn't need a real filesystem. It just needs to think it's in one.

We built ChromaFs, a virtual filesystem that translates standard UNIX commands into queries against our Chroma database. Every doc page becomes a file, every section a directory. The agent explores documentation the way a developer explores a codebase.

The results: startup dropped from 46s to ~100ms, marginal compute cost hit $0 per conversation, and output quality matched the full sandbox, with access control built in.

ChromaFs now powers 30,000+ daily conversations for hundreds of thousands of users. No containers, no cold starts, no invoice surprises.

Mintlify powers docs for 20,000+ companies, reaching 100M+ people a year. We just raised a $45M Series B led by a16z and Salesforce Ventures to keep building the knowledge layer for the agent era.

Try Mintlify today!

I used GPT IMG 2.0 + Seedance 2.0 to Generate an short AI film

OpenAI Rewires ChatGPT's Memory With "Dreaming V3"

OpenAI began rolling out a rebuilt memory architecture on 4 June, starting with Plus and Pro subscribers in the United States and extending to Free and Go users in the following weeks. The new system synthesizes context in the background across years of past conversations, automatically updates time-sensitive information, and exposes a readable memory summary page that users can edit. OpenAI says a roughly fivefold reduction in compute cost made the broader rollout economically viable, though researchers have flagged questions about how inferred memories should be audited. Source: OpenAI

Storyboard to Video

Here is how you can do it...

1. - Go to ChatGPT

2. - Give it a situation and ask it to turn it into a 3x3 Storyboard grid. 

3. - Next, ask ChatGPT to write a prompt to turn this Storyboard into a video clip

4. - Next, Go to Seedance 2.0, copy the prompt from ChatGPT

5. - Upload the storyboard, paste the prompt and hit generate

Prompt I used

Reference the storyboard exactly. Preserve panel-to-panel continuity, character design, geography, action progression, and camera direction. This is a 15-second fantasy-action chase sequence with clear escalation and a strong final payoff. Every shot must feel physically connected to the previous shot, as if captured by a real film crew. No random movement, no disconnected action, no camera teleportation.
Character: Young agile sky runner, barefoot, athletic build, weathered travel clothing, large mechanical artifact strapped to his back. Determined, resourceful, constantly under pressure.
Environment: Massive mountain canyon filled with mist, floating temple ruins, steep cliffs, ancient wooden structures, suspended bridges, atmospheric depth, dust, debris, and stormy skies. Large sense of scale.
SHOT 1 (0-1.5s)
Wide establishing shot. Camera pushes forward through mist revealing a fragile wooden bridge suspended across a gigantic canyon. The runner appears small against the scale of the world. Wind moves clothing and loose debris.
SHOT 2 (1.5-3s)
Low-angle tracking shot moving backward in front of him. He sprints directly toward camera. Bridge planks break apart behind him. Dust and splinters fill the air.
SHOT 3 (3-4.5s)
Medium side tracking shot. Massive falling logs crash across the bridge behind him. He narrowly avoids destruction without slowing down.
SHOT 4 (4.5-6s)
Close action shot. Heavy breathing. Focused eyes. Camera shakes subtly from impacts. The bridge ahead begins collapsing section by section.
SHOT 5 (6-7.5s)
Wide shot. He reaches a broken section. The path ends. A suspended rope stretches across open space. The canyon drops thousands of feet below.
SHOT 6 (7.5-9s)
Decision moment. He commits instantly. Powerful running leap. Camera follows from behind as he launches into open air.
SHOT 7 (9-10.5s)
Wide aerial shot. He falls through mist and debris. The rope swings beneath him. Extreme scale. Strong feeling of height and danger.
SHOT 8 (10.5-12s)
Dynamic close action. He catches the rope. Momentum violently swings him forward. The mechanical artifact begins transforming.
SHOT 9 (12-15s)
Hero payoff sequence. A wheel-like mechanism unfolds and locks onto the rope. Sparks erupt from friction. Camera tracks alongside at extreme speed. Dust, wind, sparks, and motion blur streak past. The rider accelerates toward a distant ancient temple city carved into the cliffs. Final shot pulls wide, revealing the vast canyon as he races into the horizon.

AI & Tech News

Microsoft Unveils Its Vision for AI Native Computing

At Build 2026, Microsoft showcased a future where AI agents become the primary interface for software and services. The company introduced new AI powered devices, expanded Copilot capabilities, and revealed in-house reasoning models designed to compete with leading frontier systems.

The announcements suggest Microsoft is moving beyond AI assistants toward fully autonomous task execution across business workflows. Source

Anthropic Expands Project Glasswing to Critical Infrastructure

On 2 June, Anthropic extended its Project Glasswing cybersecurity programme, which gives vetted organisations controlled access to its Claude Mythos model, to roughly 150 additional partners across more than fifteen countries. Newly included sectors cover power utilities, water systems, healthcare networks, telecoms and hardware manufacturers, building on a founding cohort that included AWS, Apple, Cisco, CrowdStrike, Google, JPMorgan Chase, Microsoft, NVIDIA and Palo Alto Networks. Participants have collectively reported more than 10,000 high or critical software vulnerabilities since the programme launched in April. (Source: Anthropic)

Anthropic Quietly Files the Paperwork for a Near-Trillion-Dollar IPO

Anthropic confirmed it had confidentially submitted a draft S-1 to the U.S. Securities and Exchange Commission, giving the Claude maker the option to go public once the SEC completes its review.

The filing follows a $65 billion Series H round that pushed Anthropic to a $965 billion post-money valuation, with revenue run-rate reportedly crossing $47 billion in May. Share count and pricing remain unset, but the move puts Anthropic ahead of OpenAI in the race to public markets and reshapes how investors are likely to value frontier AI labs through the rest of the year. (Source: techcrunch)

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Shailesh & OpenAILearning team