Omini

Countdown to launch. $OMINI goes live on Ju.com today. A new market, a wider reach, and another step forward for the ecosystem. OMINI/USDT March 28, 18:00 (UTC+8) #OMINI #Jucom #BASE #Web3 ju.com/en/trade/omini…

CPU vs GPU vs TPU vs NPU vs LPU, explained visually: 5 hardware architectures power AI today. Each one makes a fundamentally different tradeoff between flexibility, parallelism, and memory access. > CPU It is built for general-purpose computing. A few powerful cores handle complex logic, branching, and system-level tasks. It has deep cache hierarchies and off-chip main memory (DRAM). It's great for operating systems, databases, and decision-heavy code, but not that great for repetitive math like matrix multiplications. > GPU Instead of a few powerful cores, GPUs spread work across thousands of smaller cores that all execute the same instruction on different data. This is why GPUs dominate AI training. The parallelism maps directly to the kind of math neural networks need. > TPU They go one step further with specialization. The core compute unit is a grid of multiply-accumulate (MAC) units where data flows through in a wave pattern. Weights enter from one side, activations from the other, and partial results propagate without going back to memory each time. The entire execution is compiler-controlled, not hardware-scheduled. Google designed TPUs specifically for neural network workloads. > NPU This is an edge-optimized variant. The architecture is built around a Neural Compute Engine packed with MAC arrays and on-chip SRAM, but instead of high-bandwidth memory (HBM), NPUs use low-power system memory. The design goal is to run inference at single-digit watt power budgets, like smartphones, wearables, and IoT devices. Apple Neural Engine and Intel's NPU follow this pattern. > LPU (Language Processing Unit) This is the newest entrant, by Groq. The architecture removes off-chip memory from the critical path entirely. All weight storage lives in on-chip SRAM. Execution is fully deterministic and compiler-scheduled, which means zero cache misses and zero runtime scheduling overhead. The tradeoff is that it provides limited memory per chip, which means you need hundreds of chips linked together to serve a single large model. But the latency advantage is real. AI compute has evolved from general-purpose flexibility (CPU) to extreme specialization (LPU). Each step trades some level of generality for efficiency. The visual below maps the internal architecture of all five side by side, and it was inspired by ByteByteGo's post on CPU vs GPU vs TPU. I expanded it to include two more architectures that are becoming central to AI inference today. 👉 Over to you: Which of these 5 have you actually worked with or deployed on? ____ Find me → @_avichawla Every day, I share tutorials and insights on DS, ML, LLMs, and RAGs.

$OMINI is built for long-term network value, not short-term noise. A stronger structure, a clearer path, and now a bigger stage. See you on Ju.com. #OMINI #BASE #Web3

We’re excited to share that $OMINI will be listed on Ju.com with the OMINI/USDT trading pair going live on March 28, 2026 at 18:00 (UTC+8). Deposits and withdrawals open on March 27, 2026 at 18:00 (UTC+8). Another solid step forward for Omini on BASE. #Jucom

Early is where the upside lives. While most people wait for the noise, the smart ones notice the structure, the momentum, and the room still left to grow. Omini is still in that phase. Not crowded. Not finished. Exactly why it’s worth watching now.

Most people only notice a network when it’s already big.What they don’t see is the early phase — when things are still forming, still opening up.That’s where we are now with Omini.And usually, that’s where the real opportunities are.

Introducing 𝑨𝒕𝒕𝒆𝒏𝒕𝒊𝒐𝒏 𝑹𝒆𝒔𝒊𝒅𝒖𝒂𝒍𝒔: Rethinking depth-wise aggregation. Residual connections have long relied on fixed, uniform accumulation. Inspired by the duality of time and depth, we introduce Attention Residuals, replacing standard depth-wise recurrence with learned, input-dependent attention over preceding layers. 🔹 Enables networks to selectively retrieve past representations, naturally mitigating dilution and hidden-state growth. 🔹 Introduces Block AttnRes, partitioning layers into compressed blocks to make cross-layer attention practical at scale. 🔹 Serves as an efficient drop-in replacement, demonstrating a 1.25x compute advantage with negligible (<2%) inference latency overhead. 🔹 Validated on the Kimi Linear architecture (48B total, 3B activated parameters), delivering consistent downstream performance gains. 🔗Full report: github.com/MoonshotAI/Att…

The value of a network grows with every new connection. More nodes joining. More activity flowing. More opportunities opening. Omini is steadily expanding — and the ecosystem is just getting started. #Omini #Web3

Some networks promise the future. Others are already building it. Omini is growing node by node, connection by connection. As the network expands, so do the opportunities for everyone participating. This is how decentralized value begins to move. #Omini #Web3

The network grows quietly — but the impact keeps expanding. More nodes More connections More value moving across the ecosystem. With Omini, every new participant strengthens the network and unlocks new opportunities. The future of decentralized networks is just getting started

A strong network doesn’t appear overnight. It grows through participation, activity, and shared progress With Omini, every connection helps expand the ecosystem — and every contribution moves the network one step further The future of decentralized networks is built together

A growing network changes everything. More users. More activity. More opportunity. #Omini is designed to evolve with its community.

The early stage of any network is where the biggest momentum begins. Omini is still expanding. Which means the ecosystem is still opening new opportunities.

Start building with Gemini Embedding 2, our most capable and first fully multimodal embedding model built on the Gemini architecture. Now available in preview via the Gemini API and in Vertex AI.

Some networks reward speculation. Others reward participation. #Omini is built for the people who actually help the network move forward.

Rewards should follow contribution. That’s the principle behind Omini. When a network grows through real participation, value naturally flows back to the people inside it.

OpenClaw meets RL! OpenClaw Agents adapt through memory files and skills, but the base model weights never actually change. OpenClaw-RL solves this! It wraps a self-hosted model as an OpenAI-compatible API, intercepts live conversations from OpenClaw, and trains the policy in the background using RL. The architecture is fully async. This means serving, reward scoring, and training all run in parallel. Once done, weights get hot-swapped after every batch while the agent keeps responding. Currently, it has two training modes: - Binary RL (GRPO): A process reward model scores each turn as good, bad, or neutral. That scalar reward drives policy updates via a PPO-style clipped objective. - On-Policy Distillation: When concrete corrections come in like "you should have checked that file first," it uses that feedback as a richer, directional training signal at the token level. When to use OpenClaw-RL? To be fair, a lot of agent behavior can already be improved through better memory and skill design. OpenClaw's existing skill ecosystem and community-built self-improvement skills handle a wide range of use cases without touching model weights at all. If the agent keeps forgetting preferences, that's a memory problem. And if it doesn't know how to handle a specific workflow, that's a skill problem. Both are solvable at the prompt and context layer. Where RL becomes interesting is when the failure pattern lives deeper in the model's reasoning itself. Things like consistently poor tool selection order, weak multi-step planning, or failing to interpret ambiguous instructions the way a specific user intends. Research on agentic RL (like ARTIST and Agent-R1) has shown that these behavioral patterns hit a ceiling with prompt-based approaches alone, especially in complex multi-turn tasks where the model needs to recover from tool failures or adapt its strategy mid-execution. That's the layer OpenClaw-RL targets, and it's a meaningful distinction from what OpenClaw offers. I have shared the repo in the replies!

At CIIE, Alibaba Cloud and Dun & Bradstreet explored AI + Data for global growth! Dr. Pei Shen: Overseas insights = "Amap" for teams. AI shifts: Talent as collaborators, smart processes, iterative culture. Partnering for data-cloud innovation!

Every strong ecosystem shares one thing: People who helped build it benefited from its growth. Omini is creating a network where participation is not just activity — it’s value creation.

Many people ask where opportunity begins. In most networks, it begins with participation. Omini is designed so that early contributors are part of the system’s growth, not just observers. The network grows. The rewards grow with it.