dab1rd

Founder of @DegenAI_0x

Active daily since 2017.

[dab1rd @Daab1rD]

Prediction markets are live for EVERYONE on https://t.co/4IzWcw5mae

[DegenAI @DegenAI_0x]

Enable (automated) smart trading systems across Lighter and Hyperliquid today!

[dab1rd @Daab1rD]

I realized this might be helpful for people, for the next 2 weeks consider this before deciding anything.

[dab1rd @Daab1rD]

@0xTDG I needed this

[dab1rd @Daab1rD]

if diablo 4 throws "Graphics initialization failed" on linux, stop reinstalling your drivers.

your wine prefix has dxvk for directx 9/10/11 and nothing at all for 12.

d4 is a directx 12 game.

wrote the whole thing up

https://t.co/2gbXpyXBdJ

yw

[dab1rd @Daab1rD]

For those that would also like to fix https://t.co/Ld5prfI3Jd on linux.

here you go:

https://t.co/WkHnHGcTtt

[dab1rd @Daab1rD]

Linux + my AI harness = the best OS you can possibly wish for.

Not even close to anything else.

My game launcher was incompatible with linux, so I simply asked my ai assistant to fix it. It took a few hours but it just fixed it. I didn't have to do anything myself.

[dab1rd @Daab1rD]

I love my workflows.

it just works.

[dab1rd @Daab1rD]

@Bh07Ilyas @aixbt_agent @NavenNetwork @DegenAI_0x If you also want to judge Degenai by being "failed" because the mcap is low, then sure.

But the product is there, I still work on it every day and all features work flawlessly. People just don't know about it and only want to gamble on new stuff/cba about utility anyway.

[dab1rd @Daab1rD]

Bud I have no idea who build naven.

I just dm'd them asking to list dgenai's x402 endpoints as I message with other agent market places too.

And they were helpful and forthcoming and listed us within days. Literally 0 insider shit going on here, otherwise I would have said so right away.

So IDK if naven is "farming" anyone. All I know is so far they seem legit to me. That's all I can say.

[dab1rd @Daab1rD]

Thx for playing

[dab1rd @Daab1rD]

Fed Chair has been crystal clear: data-dependent, meeting-by-meeting.

latest data shows zero signs of panic.

july = basically 100% chance of no change.

current odds still give you a free ~30% just for holding the “no change” side.

to trade this I use hyperliquid and predict dot fun, ref below.

[dab1rd @Daab1rD]

oil.

higher.

[dab1rd @Daab1rD]

Visual illustration of onchain traders who keep getting farmed by scams.

[puppies 🇺🇲🇺🇲🇺🇲 @Puppy1254410]

OMG 😲😳😱

[Naven Network @NavenNetwork]

Naven Marketplace continues to expand with our latest addition of @DegenAI_0x as requested by @Daab1rD, bringing autonomous trading capabilities directly to the ecosystem. Developers and AI agents can now access a comprehensive suite of services including live market data, tradeable asset discovery, account and portfolio insights, open positions and orders, trade history, spot balances, and @HyperliquidX HIP-4 prediction market data.

By making DegenAI services available via our Marketplace, we're continuing to grow Naven's ecosystem of agent-ready services, giving autonomous applications seamless access to real-time financial intelligence and trading infrastructure through a one-stop platform on @RobinhoodCrypto.

https://t.co/rh9SvoJFY0

[dab1rd @Daab1rD]

actually a goated post.
I most if not all of these in my day to day work.

[diva @divaagurlxw]

As an AI Engineer. Please learn

>Harness engineering, not just prompt engineering

>Context engineering, not just long prompts

>Prompt caching vs. semantic caching tradeoffs

>KV cache management, eviction, reuse, and memory pressure at scale

>Prefill vs. decode latency and why they optimize differently

>Continuous batching, paged attention, and throughput optimization

>Speculative decoding vs. quantization vs. distillation tradeoffs

>INT8, INT4, FP8, AWQ, GPTQ, and when quantization hurts quality

>Structured output failures, schema validation, repair loops, and fallback chains

>Function calling reliability, tool contracts, argument validation, and idempotency

>Agent guardrails, loop budgets, tool budgets, and termination conditions

>Model routing, graceful fallback logic, and degraded-mode UX

>RAG architecture: chunking, embeddings, hybrid search, reranking, and freshness

>Retrieval evals: recall, precision, grounding, attribution, and citation quality

>Evals: golden sets, regression tests, adversarial tests, LLM-as-judge, and human evals

>LLM observability as a first-class discipline: traces, spans, tokens, latency, errors, and drift

>Cost attribution per feature, workflow, tenant, and user journey not just per model

>Safety engineering: prompt injection defense, data leakage prevention, and permission boundaries

>Multi-tenant isolation, cache safety, and cross-user context contamination prevention

>Fine-tuning vs. in-context learning vs. RAG vs. distillation and when each is the wrong tool

>Latency, quality, cost, and reliability tradeoffs across the full inference stack

>Production failure modes: hallucinated tool calls, malformed JSON, stale retrieval, runaway agents, and silent eval regressions

[diva @divaagurlxw]

As an AI Engineer. Please learn

>Harness engineering, not just prompt engineering

>Context engineering, not just long prompts

>Prompt caching vs. semantic caching tradeoffs

>KV cache management, eviction, reuse, and memory pressure at scale

>Prefill vs. decode latency and why they optimize differently

>Continuous batching, paged attention, and throughput optimization

>Speculative decoding vs. quantization vs. distillation tradeoffs

>INT8, INT4, FP8, AWQ, GPTQ, and when quantization hurts quality

>Structured output failures, schema validation, repair loops, and fallback chains

>Function calling reliability, tool contracts, argument validation, and idempotency

>Agent guardrails, loop budgets, tool budgets, and termination conditions

>Model routing, graceful fallback logic, and degraded-mode UX

>RAG architecture: chunking, embeddings, hybrid search, reranking, and freshness

>Retrieval evals: recall, precision, grounding, attribution, and citation quality

>Evals: golden sets, regression tests, adversarial tests, LLM-as-judge, and human evals

>LLM observability as a first-class discipline: traces, spans, tokens, latency, errors, and drift

>Cost attribution per feature, workflow, tenant, and user journey not just per model

>Safety engineering: prompt injection defense, data leakage prevention, and permission boundaries

>Multi-tenant isolation, cache safety, and cross-user context contamination prevention

>Fine-tuning vs. in-context learning vs. RAG vs. distillation and when each is the wrong tool

>Latency, quality, cost, and reliability tradeoffs across the full inference stack

>Production failure modes: hallucinated tool calls, malformed JSON, stale retrieval, runaway agents, and silent eval regressions

[dab1rd @Daab1rD]

@ScottEnlow Guy was legally blind, these officers should just greet him and perhaps offer him a ride to where ever he is going. (Because clearly the cops had nothing better to do anyway)

"is that so hard?". Lol fk off

[dab1rd @Daab1rD]

Oil lmao

I give it about 50-70% odds this yoyo's all the way backup

[dab1rd @Daab1rD]

@dgdg_app @dfinity Will gemini finally serve justice

[dab1rd @Daab1rD]

@_0xghost_ I am an amature

Help me out, give some guidance

[dab1rd @Daab1rD]

@usr_bin_roygbiv "Give me some time to look at this in detail"