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Introducing Software Factory Unified Agent. The Software Factory agent now operates across all modules in the assembly line, rather than one agent per module. Users can move with fluidity retaining their conversation history at each step from requirements, to blueprints, to work orders. Skills and alerts are tagged by...

135,944 views • 24 days ago •via X (Twitter)

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Ford CEO Jim Farley on why it's so difficult for legacy car companies to get software right & why Tesla’s vertically integrated approach is the right one: “We farmed out all the modules that control the vehicles to our suppliers because we could bid them against each other, so Bosch would do the body control module, someone else would do the seat control module, someone else would do the engine control module. We have about 150 of these modules with semiconductors all through the car. The problem is the software are all written by you know 150 different companies and they don't talk to each other. So even though it says Ford on the front, I actually have to go to Bosch to get permission to change their seat Control software. So even if I had a high-speed modem in the vehicle and and I had the ability to write their software, it's actually their IP and I have 150, we call it the loose Confederation of software providers, 150 completely different software programming languages, you know all the structure of the software is different. It’s millions of code and we can't even understand it all. That's why at Ford we've decided in the second generation product to completely insource electric architecture. To do that you need to write all the software yourself, but just remember car companies have never written software like this, ever, so we're literally writing how the vehicle operates the software to operate the vehicle for the first time ever.” via Everything Electric Show:

Sawyer Merritt

1,172,560 views • 1 year ago

Introducing LobeHub: Agent teammates that grow with you. LobeHub is the ultimate space for work and life: to find, build, and collaborate with agent teammates that grow with you. We’re building the world’s first and largest human–agent co-evolving network. Two years ago, we built LobeChat, an open-source interface for using different AI models. Today, LobeChat has 70k+ GitHub stars and serves 6M+ users worldwide. How to fully unlock the power of models has always been a shared mission between us and the community. We started with interaction — a fundamentally new, agent-first experience. Agents are no longer passive tools invoked in a single conversation. They should be proactive, always-on units of work. Treating agents as the minimal atomic unit is also the core of our agent harness infra. Today’s agents are mostly one-off executors. Even with memory, it’s often global — and hallucinates. We build long-term agent teammates that evolve with users. Each agent has its own dedicated memory space, editable by users, allowing humans and agents to co-evolve over time. This, in turn, allows us to design clearer rewards for reinforcement learning and create cleaner environments for continual learning. Agent teammates can work in groups. Through a multi-agent system, agent groups operate faster, more cost-effective, and go beyond what single-agent systems can achieve. For example, a single agent often requires heavy user involvement to proceed step by step, whereas LobeHub can execute the same work from a single instruction, with a supervisor orchestrating agents that run in parallel or debate to produce better results. We are building the collaboration network among agent teammates — and between humans and agent teammates as well. Ease of use matters. AI intelligence and shared human intelligence are equally important. With simple instructions and tool selection, you can effortlessly build and team up with agent coworkers to deliver complex, systematic work — even assembling a quant team to execute trades. Through the LobeHub community, anyone can discover, reuse, and remix agents and agent groups, customizing them to fit their own workflows, preferences, and needs. Last but not least, our vision started with LobeChat: multi-model support is the most efficient approach for users. We believe different models excel in different scenarios. By routing across multiple models, LobeHub improves cost efficiency and unlocks capabilities that a single-model setup cannot easily support.

LobeHub

185,013 views • 4 months ago