AI

print(“n### SECTION D: end-to-end Transformer (vanilla fp32 vs Apex fused + AMP) ###”) VOCAB, D, NHEAD, LAYERS, SEQ, BATCH, STEPS = 2000, 256, 4, 4, 128, 32, 60 class Block(torch.nn.Module): def __init__(self, d, nhead, norm_cls): super().__init__() self.attn = torch.nn.MultiheadAttention(d, nhead, batch_first=True) self.ff = torch.nn.Sequential(torch.nn.Linear(d, 4 * d), torch.nn.GELU(), torch.nn.Linear(4 * d, d)) self.n1, self.n2 = … Read more

Hermes Agent already remembers from every session. An open source agent from Nous Research ships with selected memory files and full-text session search. But a new community project says the built-in memory is too shallow to handle heavy workloads. A new library called ‘Memory OS’ has been released under the MIT license by the developer … Read more

The Transformer’s focus has not changed since 2017. Most efficient work has tried to replace the softmax focus directly. The new paper takes a different route. Maintains softmax attention and bolts on the repair branch. A team of researchers from Northwestern University, Tilde Research, and the University of Washington present a Local Linear Attention called … Read more

scenarios = [ { “name”: “Safe database read”, “tool”: research_db, “kwargs”: { “table”: “customers”, “operation”: “select”, “type”: “select”, “sensitivity”: “medium” } }, { “name”: “Blocked destructive database action”, “tool”: research_db, “kwargs”: { “table”: “customers”, “operation”: “drop”, “type”: “drop_table”, “sensitivity”: “critical” } }, { “name”: “External email requiring approval”, “tool”: research_email, “kwargs”: { “to”: “[email protected]”, “recipient_domain”: … Read more

banner(“5) Evaluate skills on 5 quality dimensions (quality gate)”) DIMS = [“safety”, “completeness”, “executability”, “maintainability”, “cost_awareness”] LEVEL_SCORE = {“Excellent”: 4, “Good”: 3, “Fair”: 2, “Poor”: 1, “Bad”: 0} def evaluate(target): if USE_SDK and API_KEY: try: return client.evaluate(target=target) except Exception as e: print(f” evaluate failed for {target}: {e!r}”) return None def mock_eval(name): import hashlib h = … Read more

Genesis AI released Genesis World 1.0. The platform consists of four components: the Genesis World physics engine, Nyx (a real-time tracked renderer), Quadrants (a Python-to-GPU compiler), and a simulation interface. It is designed to accelerate the development of a robotics foundation model through simulation-based simulation. Robot model development has two constraints: data and iteration speed. … Read more

Nous Research’s open source Hermes Agent now ships with a Search Tool feature. It directly addresses a growing bottleneck in AI agent systems: many MCP tools fill the context window. In this introductory article, we’ll reveal what Tool Search does, how it works, and when to use it. Problem: MCP Tools Are Eating Your Window’s … Read more

The AI ​​industry has grown to that point Raw intelligence is no longer the only thing that matters. Last year, each model release was a race to publish big benchmark numbers. More parameters, features and everything in between. Today, the conversation is changing. Developers care about reliability. Businesses care about cost, scalability, and whether the … Read more

Most AI agents stop improving when people stop tuning them. The model is fixed. The scaffolding around us has been repaired. Hexo Labs wants to deliver both at the same time. It released SIA (Self-Improving AI) this week as an open source framework under the MIT license. The core claim of this study is small … Read more

Liquid AI just shipped LFM2.5-8B-A1B. It’s a Mixture-of-Experts (MoE) device model built for toolkits. The model holds a total of 8.3B parameters but activates only 1.5B per token. That sparsity is what allows it to run on consumer hardware. The release follows the LFM2-8B-A1B, which the Liquid AI team previously published. LFM2.5 is a new … Read more