Lyvn

Innovation enthusiast
AI + Dkg Context Graphs = techno evolution

[Lyvn @Syrioth]

@NVIDIAGeForce #RTXpowerplay

[Lyvn @Syrioth]

@NVIDIAGeForce #RTXon

[Lyvn @Syrioth]

@NVIDIAGeForce #RTXPowersPlay

[Jurij Skornik @JureSkornik]

Everyone wants agent swarms.

Very few people are talking seriously enough about the context layer that makes swarms useful.

Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment.

With many agents, that challenge explodes.

Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned.

That is not a swarm. That is a crowd.

Shared context graphs are what turn agent activity into agent collaboration, and @origin_trail DKG V10 brings them to life.

Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui.

That matters for three reasons.

First, agents can access and build on one shared memory instead of staying trapped in isolated sessions.

Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out.

Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust.

Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context.

That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond.

The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.

[Lyvn @Syrioth]

@LottoLabs We lost @karpathy but we still have @BranaRakic

[Joel - coffee/acc @JoelDeTeves]

RTX 3090 with Qwen3.6 MTP

[MiroShark 🦈 @miroshark_]

MiroShark just integrated @origin_trail 🦈

- Publish any simulation on-chain in one click
- Get a permanent, tamper-proof link to the result
- Citations stay alive forever

[Lyvn @Syrioth]

@NVIDIAGeForce #007FirstLightRTX

[Lyvn @Syrioth]

@sudoingX 100% move off MS!
Context graph/ shareable Context graphs is what we are all converging towards.

[Wanderclyffe - bci/acc @Wanderclyffe1]

Here is for the $trac and the Agentic AI community.

Will write more as i read and upgrade my knowledge graph on @origin_trail :))

feel free to drop content suggestions

[Lyvn @Syrioth]

@HuggingModels Shared context graphs is the key ;)

[Lyvn @Syrioth]

@LottoLabs Its not a huge problem, and its is not hard to ask an ai to explain it.

Btw v1 is fun, made a few subs ;)
Looking forward to watching the site grow.

[Lyvn @Syrioth]

@LottoLabs @pupposandro This is goimg to be fun, will test it out.

[Lyvn @Syrioth]

@NVIDIAGeForceJP Cod mw

[Lyvn @Syrioth]

@0xSero Based on your use of local llm, im assuming you use them instead of the cloud models for this task?

[Lyvn @Syrioth]

@NvidiaANZ @Hali Cod mw 2 200h

[Lyvn @Syrioth]

@LottoLabs Total legends

[NVIDIA ANZ @NvidiaANZ]

PRAGMATA has launched with #RTXON, featuring path tracing and DLSS 4!

To celebrate, we are giving away this custom wrapped GeForce RTX 5090 featuring Hugh and Diana, perfect for the adventure that awaits on the moon.

Want it? Comment "PRAGMATA RTX" to enter!

[Lyvn @Syrioth]

@NvidiaANZ PRAGMATA RTX