Adam Julius

[Adam Julius @AdamJJulius]

@Tessera_PE real alpha

[Tessera Lab @Tessera_PE]

The IPO gets the headlines, the alpha is getting access before it happens.

[Adam Julius @AdamJJulius]

@Tessera_PE full IPOs coming to @solana

[Peter Steinberger 🦞 @steipete]

lol did nobody at Anthropic stop for a second and wonder why the numbers looked this absurd before posting the “victory”-tweet?
https://t.co/DPdPc04YZT

[Adam Julius @AdamJJulius]

@Tessera_PE RWA empire

[Tessera Lab @Tessera_PE]

Rome took centuries. Private equity takes a tap.

[Adam Julius @AdamJJulius]

@Tessera_PE good read

[Tessera Lab @Tessera_PE]

https://t.co/tYjUWV0UUb

[Adam Julius @AdamJJulius]

@Tessera_PE 🔥

[Tessera Lab @Tessera_PE]

Solana is scaling RWAs. Tessera is opening the private equity layer.

[slash1s @slash1sol]

A TEAM JUST DEPLOYED 15 AUTONOMOUS LOOP AGENTS FROM A SINGLE PROMPT USING APPLIED GRAPH ENGINEERING

Most developers still manually hardcode multi agent systems, writing separate logic for every individual task.

Graph engineering changes this by using a central topological map to spin up all 15 nodes simultaneously.

A single 200 word input generates the architecture, routing 120 unique pathways between agents instantly.

Instead of failing under conflicting instructions, these loop agents self correct via continuous state sharing.

Managing a 15 node mesh requires high token throughput, making this dependent on strict low-] latency API tiers.

See exactly how this automated multi agent graph architecture actually operates in real time ↓

[Codez @0xCodez]

A senior Anthropic engineer just dropped 12-page PDF on "Graph Engineering" for multi-agentic systems.

The shift: your agents memory dies with their context window. A knowledge graph makes it permanent.

Extract → Resolve → Assemble → Query → Repeat

Every agentic graph has 5 stages:

• Extract: Haiku pulls entities and S-P-O triples. One call per doc. The Pydantic schema is the only training data.

• Resolve: Sonnet clusters "Edwin Aldrin" → "Buzz Aldrin" - zero string overlap - using descriptions as context.

• Assemble: canonical nodes, typed edges, provenance on every triple. One connected graph.

• Query: serialize a subgraph → Sonnet reasons over triples → every answer cites a specific edge.

Plug this into multi-agent systems as shared memory.

Workers write to it, evaluators fact-check against it, loops persist overnight with it.

This 12-page PDF changed how I'm building multi-agent systems today.

Read it now, then explore the article below.

[vartekx @vartekxx]

Boris Cherny just dropped 7-page PDF on Graph Engineering - how 4 Claude prompts replace 4 trained ML models

The twist: your agent's memory dies with the context window. A knowledge graph makes it permanent - and now you build one with prompts, not ML engineers.

here's 4 prompts, step by step:

prompt 1 → extraction - Haiku pulls entities + relations - one call per doc - no NER, no labeled data

prompt 2 → resolution - Sonnet merges duplicates string matching will never catch - two different names, same person

prompt 3 → summarization - Sonnet builds profiles from multiple sources - facts that never appeared in the same document

prompt 4 → querying - feed the graph to Sonnet - every answer cites a specific edge - no hallucination

how to wire this into agents today:

step 1 → shared memory for multi-agent teams - workers read/write one graph - orchestrator's context stays clean

step 2 → grounding layer for eval loops - evaluator checks facts against graph edges, not vibes

the result: one Pydantic schema replaces weeks of ML training per domain - precision 1.00 - 10k docs cost under $10

this 7-page PDF is what comes after loop engineering

bookmark this, then read the article below ↓

[Adam Julius @AdamJJulius]

@Tessera_PE bullish, haven’t seen a nice graph in a while

[Tessera Lab @Tessera_PE]

Internet Private Equity Markets next

[Adam Julius @AdamJJulius]

@Tessera_PE next stop tessera

[Tessera Lab @Tessera_PE]

Ring the bell early. Trade private equities.

[Adam Julius @AdamJJulius]

@Tessera_PE 40B easy

[Tessera Lab @Tessera_PE]

https://t.co/4kF3p3i0qU

[Adam Julius @AdamJJulius]

@Tessera_PE dca spacex feel like tesla merger in the future