Robiniflore

so busy

[Robiniflore @Robiniflore]

Will apps really be replaced or pushed to the background?

[Robiniflore @Robiniflore]

Which is why agents will, and need to, stand on equal footing with human users in the digital world. The center of gravity shifts from user-centric to agent-centric.

Digital employees are the next wave to land — and the next big opportunity.

[Robiniflore @Robiniflore]

Single LLM call → simple tool loop → complex agent execution → agent execution deeply wired into private data.
From stateless blank slate, to copilot, to driver — every step up in model capability expands the agent's power, and with it, its responsibility.

[Robiniflore @Robiniflore]

The harness needs an explicit layer for external event semantics.

[Robiniflore @Robiniflore]

Building a digital employee is a fundamentally different problem from building an LLM/agent-powered business app. The key differences:

[Robiniflore @Robiniflore]

Digital employee: the center isn't the user, it's the employee itself. It must keep interacting with multiple users across multiple channels simultaneously. That means the LLM has to disambiguate external inputs by type and source—native context alone can't carry that complexity.

[Robiniflore @Robiniflore]

Users don't need yet another all-powerful agent. They need tools that solve real problems — a complete application layer built around actual business needs.

[Robiniflore @Robiniflore]

But once a use case becomes a real, ongoing business (2B or 2C), the surprises dry up. Stability and usability take back the spotlight — and any wobble becomes a thorn in your side you can't tolerate. (2/2)

[Robiniflore @Robiniflore]

Humans are wired to notice the unexpected — that's why LLMs can get away with being wildly unstable in certain scenarios. Their very existence is a surprise, and their outputs are full of them. (1/2)

[Robiniflore @Robiniflore]

End-to-end vibe: good-enough requirements, good-enough implementation, good-enough delivery — and in the end, it's just... good enough to use 😬

[Z.ai @Zai_org]

Introducing GLM-4.5 and GLM-4.5 Air: new flagship models designed to unify frontier reasoning, coding, and agentic capabilities.

GLM-4.5: 355B total / 32B active parameters
GLM-4.5-Air: 106B total / 12B active parameters

API Pricing (per 1M tokens):
GLM-4.5: $0.6 Input / $2.2 Output
GLM-4.5-Air: $0.2 Input / $1.1 Output

Tech Blog: https://t.co/rg5nrMdab7
Weights: https://t.co/cGCGnB735L
https://t.co/DRCwFHQQRn API: https://t.co/pqo3sYqh9K
OpenRouter: https://t.co/nXjmxr9Qsk
Develop Tools: https://t.co/dBJ9KU9Kyk
Try them now: https://t.co/WCqWT0raFJ