Paradatum

Congrats to the DeFacts team who used Paradatum deterministic AI to move to Round 2 with their ETHGlobal Hackathon Submission! Friends don't let friends use non-deterministic AI! ETHGlobal | Open Agents ethglobal.com/events/openage…

BREAKING: Former NJ mayoral candidate Henrilynn Ibezim (D) pleaded guilty to forging nearly 1,000 voter registration applications The thing that never happens happened again!

“If you wish to make an apple pie from scratch, you must first invent the universe.” — Carl Sagan

"The only way to build something that everyone thinks is impossible is to not realize it’s impossible in the first place. Conviction is the ultimate engine of progress." — Palmer Luckey

max⁡(∣x∣,∣y∣,∣z∣,∣w∣)=1 You’re witnessing a 2D projection of a 3D shadow cast by a 4D tesseract. As it rotates through the W-axis, the "inner" and "outer" cubes swap roles: a spatial inversion that feels like a glitch only because our biology is trapped in 3-space. It’s a hauntingly beautiful reminder that our "reality" is often just a lower-dimensional cross-section of a much more complex structure. Perspective is everything. This is used in high-dimensional data visualization, hypercube topologies in parallel computing networks, and exploring the geometry of extra dimensions in theoretical physics.

Nick Saban's 5 Enemies of Greatness. Tack them on a wall.

accurate

@vkhosla Lossy quantization is not compression though. We do actual lossless, determinsitic compression. They can call us when they want to see a 16GB model running losslessly in 8GB of vram, deterministic and FASTER than baseline.

Better compression , faster, smaller, more energy efficient than what Google announced yesterday.

@itsoliviasco @jenzhuscott @HBO Not always

compression always came with tradeoffs until now. TurboQuant hit zero degradation on the benchmarks that matter most for real use. that changes the math on what you can run and how cheaply you can run it. and you can apply it to existing models without retraining or touching the weights. something like this ships fast.

When I was consulting for @HBO Silicon Valley, zero-loss compression was the holy grail Richard Hendricks chases that perfect middle-out algo could shrink everything w/out breaking a single bit. Google just did something even more practical for the AI era: TurboQuant compresses LLM key-value caches down to 3 bits per value using random orthogonal rotation + PolarQuant scalar quantization & optional 1-bit QJL residual correction. =>> 6× memory reduction, up to 8× faster attention (on H100), & 0 degradation on LongBench, Needle-in-a-Haystack, and RULER for models like Gemma. No retraining, no calibration needed. Fiction just got out-engineered by reality. 😅💚💚

@jenzhuscott @HBO Meh. It’s lossy. Real compression is both lossless and faster to compute.

@DaveShapi @waseemhnyc Or just use paradatum as the deterministic ai inference layer. Same inputs. Same outputs. Anywhere. Every time.

> So far scaling is proving to be effective but if we never actually solve for some of the unpredictability and non determinism of LLMs, wouldn't we always need humans? There's no physical law that prevents machines from surpassing humans on all dimensions. I would not assume that LLMs are the final form

@French_Jim Mkay. Or just use paradatum as the deterministic ai inference layer. Same inputs. Same outputs. Anywhere. Every time.

SAAS isn't being replaced by AI. Structured data will continue to exist for determinism.

@zengxianyu18 Or just use paradatum as the deterministic ai inference layer. Same inputs. Same outputs. Anywhere. Every time.

Excited to see real-time CG2Real! My guess: UNet + L1/GAN trained on GT from a slow diffusion model. Determinism = temporal coherence for free. We built phase-preserving diffusion for CG2Real with high structural fidelity. Next: distill it into a fast L1+GAN network.

@LevelToPower @aakashgupta Yes. Exactly. You sacrifice determinism for lossy garbage…. But you also get vendor lock into that lossy garbage

Not really. This isn’t “Blackwell is faster.” It’s “NVIDIA is redefining efficiency.” NVFP4 isn’t portable compression — it’s a format only Blackwell can execute. Everyone else has to inflate it back to 16-bit to run. That’s not optimization, it’s bottleneck relocation. The 99.4% accuracy headline misses the point. The real shift is this: efficiency now means which chip gets to interpret the bits That’s smart vendor lock-in. Very NVIDIA. But it’s a step backward for an open, portable AI stack. As a side note, 99.4% accuracy is like 99.4% bulletproof glass. Maybe it’s useful… but I sure wouldn’t trust my life to it.

NVIDIA just told you Blackwell is mandatory for efficient inference and nobody’s repricing. The paper shows 99.4% accuracy going from 16-bit to 4-bit. That’s the headline. Here’s what matters: NVFP4 is a Blackwell-native format. The Tensor Cores on Blackwell GPUs handle FP4 operations directly with zero dequantization overhead. Every other GPU has to upconvert those 4-bit weights to 16-bit before computing. The math: ∙ 3.5x memory reduction vs FP16 ∙ 1.8x reduction vs FP8 ∙ 2.3x faster inference throughput But only on Blackwell. Run NVFP4 on an H100? You lose the throughput advantage. The weights decompress, the memory bandwidth chokes, and you’re back to FP8 economics. This tells you everything about NVIDIA’s inference strategy. They’re not just selling compression research. They’re building format lock-in into the silicon. Every model checkpoint published in NVFP4 becomes a Blackwell advertisement. The paper authors include Bryan Catanzaro and Song Han. This came from NVIDIA Research with production intent baked in. Hugging Face already hosts NVFP4 checkpoints for DeepSeek-R1, Llama 3, and FLUX. Cloud providers have two choices: upgrade to Blackwell and serve at half the memory cost, or run legacy hardware at 2x the footprint. The ROI math forces the upgrade. Anyone reading this as “cool compression paper” is missing the game. NVIDIA just made their latest silicon the only efficient way to run quantized inference at scale.

@loujaybee Or just use paradatum as the deterministic ai inference layer. Same inputs. Same outputs. Anywhere. Every time.

It's very cool to see various software factory implementations coming to life. Love this idea of wrapping deterministic workflows around prompts.

@twlvone @asmah2107 Or just use paradatum as the deterministic ai inference layer. Same inputs. Same outputs. Anywhere. Every time.

Circuit breaker and blast radius limiter come straight from distributed systems (Netflix Hystrix, 2012). What's genuinely new for agents: semantic idempotency — LLM outputs aren't deterministic so you can't just retry blindly. And the hardest one: rollback planning before execution, not after. Agents that can undo are leagues ahead.

Agentic system design concepts I'd master if I wanted to build AI that doesn't blow up in prod. Bookmark this. 1. Agent Circuit Breaker 2. Blast Radius Limiter 3. Orchestrator vs Choreography 4. Tool Invocation Timeout 5. Confidence Threshold Gate 6. Context Window Checkpointing 7. Idempotent Tool Calls 8. Dead Letter Queue for Agents 9. LLM Gateway Pattern 10. Semantic Caching 11. Human Escalation Protocol 12. Multi-Agent State Sync 13. Replanning Loop 14. Canary Agent Deployment 15. Agentic Observability Tracing Follow @asmah2107 for a future deep-dive on each.

@elonmusk @succi_edwards @niccruzpatane Or just use paradatum as the deterministic ai inference layer. Same inputs. Same outputs. Anywhere. Every time. Oh. And use less vram and get faster token per second. Yes, seriously. DM for early access

@succi_edwards @niccruzpatane Neural nets with trillions of parameters are highly resilient to bit flips, as they already contain a lot of noise. Deterministic parts of the code, such as the operating system, are already addressed by various methods like triple-voting redundancy.

SpaceX says it aims to deploy up to 1 million orbital satellite data centers in space to provide enough compute power for large-scale AI inference and training. • Satellites will get constant power in space. • No batteries are needed. • No glass framing required, which makes the solar panels cheaper to produce. • Passive Radiative cooling Elon says space will be the lowest-cost place to put AI within 3 years.

@cathrynlavery @lkr Or just use paradatum as the deterministic ai inference layer. Same inputs. Same outputs. Anywhere. Every time.

@lkr I've also found that forcing it to use Python scripts can be a game changer, so you don't rely on it following a md file the same everytime. get closer to deterministic vs probabilistic.

How to cut your AI agent's prompt size by 85% without losing any capability. Most skills dump everything into one file. Problem: the agent loads ALL of it every time, even when 80% is irrelevant. The fix takes 10 minutes: 1. Identify the "modes" in your skill 2. Write a routing table — just enough for the agent to pick the right mode 3. Move each mode's details into its own reference file 4. Agent reads the router, then reads only the file it needs Before: 359 lines loaded every invocation After: 55-line router + 1 of 7 refs loaded per use This is progressive disclosure applied to agent prompts. Same concept from UI design — show the minimum needed to make a decision, reveal details on demand. 👇example below

@CopilotKit Or just use paradatum as the deterministic ai inference layer. Same inputs. Same outputs. Anywhere. Every time.

✨ Introducing LLMock: A deterministic mock LLM server for testing Test your AI powered apps reliably, without burning money on real API calls or fighting non-deterministic outputs in CI. Open-sourced for the community. llmock.copilotkit.dev