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Good morning ☀️, leader of the next generation.
Technology is reshaping how the world works.
The real question is not what it can do — but how we choose to use it.
We focus on using technology to support human evolution.
Progress works when responsibility stays human.
⚡ WHAT'S AT STAKE TODAY ⚡
- 🤖🧠 Reload wants to give your AI agents a shared memory
- 🚀💰 Freeform raises $67M Series B to scale up laser AI manufacturing
- 💻⚖️ For open source programs, AI coding tools are a mixed blessing
- 👥🤝 Co-founders behind Reface and Prisma join hands to improve on-device model inference with Mirai
- 🏆📈 Google's new Gemini Pro model has record benchmark scores — again
- 🛒🔍 Reddit is testing a new AI search feature for shopping
- 🇮🇳🌱 Nvidia deepens early-stage push into India's AI startup ecosystem
- 📺💬 YouTube's latest experiment brings its conversational AI tool to TVs
- 💼🏛️ General Catalyst commits $5B to India over five years
Reload raises $2.275M to manage AI employees with shared memory
Reload wants to give your AI agents a shared memory
The way we work with AI is evolving rapidly. What started as simple tool usage has transformed into something resembling actual teamwork, according to Newton Asare, CEO of Reload. This shift led him and co-founder Kiran Das to create a solution for managing AI agents as digital employees rather than just automated scripts.
Reload, an AI workforce management platform launched last year, announced Thursday it has raised $2.275 million in funding led by Anthemis, with participation from Zeal Capital Partners, Plug and Play, Cohen Circle, Blueprint, and Axiom. Alongside the funding, the company unveiled Epic, its first AI product designed to solve a critical problem in AI agent deployment.
The core issue Reload addresses is the lack of coordination between AI agents. Currently, organizations use multiple agents simultaneously for various tasks like coding, debugging, and system maintenance. However, these agents operate in isolation with only short-term memory, losing context over time and potentially working against each other's efforts.
"Reload acts like the system of record for AI employees, providing visibility, coordination, and oversight as agents operate across functions," Asare explained. The platform allows organizations to connect agents regardless of their origin, assign roles and permissions, and monitor their performance across teams and departments.
Epic, built on the Reload platform, functions as an architectural overseer for software development teams. It maintains continuous awareness of product requirements and constraints, ensuring coding agents understand not just what they're building, but why. This persistent memory system prevents agents from drifting away from project goals as systems evolve.
"In software development specifically, coding agents can generate large amounts of code, but they don't preserve shared system understanding over time," Asare noted. "Epic complements those agents by defining the system upfront and maintaining shared context as it evolves."
The solution integrates directly into existing development environments, installing as an extension in AI-assisted code editors like Cursor and Windsurf. When teams begin projects, Epic creates foundational system artifacts including product requirements, data models, API specifications, and structured task breakdowns that serve as blueprints for coding agents.
As development progresses, Epic maintains structured memory of decisions, code changes, and patterns. This ensures continuity when teams switch between different coding agents or when multiple engineers work on the same project using various AI tools. Everyone builds against the same shared source of truth.
The AI infrastructure space faces intense competition from companies like LongChain, which focuses on agent deployment and memory management, and CrewAI, which helps enterprises manage AI agents. However, Das, serving as CTO, distinguishes Reload's approach by emphasizing system definition and project-level context maintenance.
"Traditional workforce systems weren't designed for AI agents operating as teammates," Das explained. "That's the layer we're focused on."
Both founders bring experience from their previous company that was successfully acquired, making Reload their second venture together. The fresh capital will fund hiring and product development, specifically expanding infrastructure to support growing numbers of AI agents across organizations.
As AI agents become more sophisticated and prevalent in workplace environments, Reload positions itself at the intersection of workforce management and AI coordination. "We're building for the next era of work," Asare concluded, reflecting the company's vision of AI agents as legitimate members of organizational teams rather than mere automation tools.
🔍 Which AI Dilemma Should We Tackle First?
- ⚠️ 1. Speed vs. Understanding The rapid pace of AI development is outpacing our ability to comprehend or regulate it.
- 🧠 2. Lack of Alignment AI systems don’t necessarily optimize for what’s good for humans—even when they seem to.
- 🏢 3. Power Concentration AI is consolidating power into the hands of a few tech giants and governments.
- 🤖 4. Automation Without Purpose AI is replacing jobs faster than society is creating meaningful alternatives.
- 🛑 5. Loss of Human Agency We risk becoming passive consumers of AI decisions, losing creativity and independent thinking.
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Freeform secures $67M to revolutionize AI-powered metal 3D printing
Freeform raises $67M Series B to scale up laser AI manufacturing
Freeform, a startup developing AI-native 3D metal printing systems, secured $67 million in Series B funding from investors including Nvidia's NVentures, Founders Fund, and Threshold Ventures.
The company plans to upgrade from its current 18-laser GoldenEye system to "Skyfall," featuring hundreds of lasers capable of producing thousands of kilograms of metal parts daily. Founded by former SpaceX engineer Erik Palitsch, Freeform leverages real-time physics simulations and advanced GPUs to optimize manufacturing processes, already delivering hundreds of mission-critical components weekly to undisclosed customers.
🎙 New Episode: Turn Views Into Revenue with Ivan Unfiltered
Most businesses are posting content…
But very few are turning it into revenue.
In this episode, I sit down with Ivan Unfiltered — founder of Viral Video Labs and the force behind one of the biggest podcasts coming out of Las Vegas.
Ivan doesn’t just create content. He builds content systems that convert.
Through Viral Video Labs, he helps entrepreneurs and brands:
- Stop the scroll
- Capture real attention
- Turn short-form video into leads, sales, and authority
We break down:
- 🔥 Why most businesses fail at short-form
- 🔥 The difference between viral and profitable
- 🔥 How to build a repeatable content machine
- 🔥 The future of short-form media
If you’re serious about growing your brand online — this episode is a must-watch.
👉 Explore the Supercharged Podcast⚡ Trends for the Future
General Catalyst commits $5B to India over five years
Silicon Valley VC firm dramatically expands India investment commitment.
General Catalyst, a prominent Silicon Valley venture firm managing over $43 billion in assets, has announced a massive $5 billion investment commitment to India's startup ecosystem over the next five years. This represents a dramatic tenfold increase from the firm's previous allocation of $500 million to $1 billion for the region.
The announcement, made at the India AI Impact Summit in New Delhi, comes less than two years after General Catalyst merged with local venture firm Venture Highway. The investment will focus on startups across artificial intelligence, healthcare, defense technology, fintech, and consumer technology sectors.
India's strategic positioning as a global AI hub is attracting unprecedented attention from international investors. With over a billion internet users and ambitious government plans to attract more than $200 billion in AI infrastructure investments over two years, the country represents a massive opportunity for tech companies and venture capitalists alike.
"India will build the next generation of global platform companies," said General Catalyst CEO Hemant Taneja, emphasizing the firm's confidence in Indian founders' ability to develop technology for large-scale markets. The firm believes India's greatest AI opportunity lies in real-world deployment rather than developing frontier AI models, citing the country's robust digital infrastructure, vast domestic market, and skilled talent pool.
The investment surge coincides with major infrastructure announcements from Indian conglomerates Adani Group and Reliance Industries, which plan to invest over $200 billion combined in AI data center infrastructure. OpenAI has partnered with Tata Group's TCS to develop a 100-megawatt AI data center, while tech giants Amazon, Google, and Microsoft have outlined tens of billions in cloud and AI investments.
General Catalyst's existing India portfolio includes companies like Zepto, PB Health, Raphe, and Jeh Aerospace. Neeraj Arora, the firm's CEO for India, Middle East, and North Africa, noted that this increased commitment allows them to support companies from early stage through public markets, operating at an entirely different scale in the region.
⚡ Let’s Make AI Actually Useful:
What Would Move the Needle in *Your* Industry?
AI has potential — but generic advice rarely helps.
What would be genuinely valuable for AI to do in your industry right now?
• Automate a painful workflow?
• Improve decision-making?
• Replace a manual process that wastes time?
• Help your team upskill faster?
Tell us what you’d want AI to handle — or where you feel stuck.
We’re using these insights to curate **industry-specific trainings, live webinars, and practical guidance** you can actually apply.
AI will be humanity's greatest collaborative partner, amplifying our collective intelligence to solve problems that seemed impossible and create innovations that benefit everyone.
Nick Frosst is the co-founder and CTO of Cohere and a former researcher at Google Brain, where he worked under Geoffrey Hinton on fundamental deep learning research including neural network compression and distillation techniques. His work on making AI models more efficient and interpretable has advanced both the science and practical deployment of language models, and he continues to advocate for building AI systems that are not only powerful but also understandable and controllable for enterprise applications.
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