cd/entity/Llama 3.3 70B· home entities Llama 3.3 70B
grep -l @llama 3.3 70b /news/*.json | wc -l → 25

Llama 3.3 70B

mentions 25 type Person page 1/2 feed RSS

// recent coverage 25 mentions

15:01
2026-06-29
dev.to
ai-agents

Writing API docs an AI agent can actually consume

FamNest engineer discovered that AI agents fail to call APIs correctly when documentation is written for humans, not machines. The coach agent in FamNest's agent graph repeatedly constructed malformed…

04:41
2026-06-24
lesswrong.com
ai-safety

Can weak AI watch strong AI?

A new experiment tested whether weaker AI models can effectively monitor stronger coding agents for malicious behavior, finding that detection rates improve with monitor size but vary by threat type. …

05:36
2026-06-18
dev.to
machine-learning

Integrating LLM with Other Machine Learning Models

A developer built a support ticket intelligence pipeline that combines Oxlo.ai embeddings, a local random forest classifier, and an LLM to automate triage and draft contextual replies. The system uses…

09:38
2026-06-17
dev.to
large-language-models

Optimizing LLM Model Performance: Best Practices and Techniques

Oxlo.ai outlines best practices for optimizing large language model performance in production, emphasizing prompt design, model selection, and request architecture. Techniques include deduplicating st…

03:36
2026-06-17
dev.to
large-language-models

Few-Shot Learning with LLM: A Deep Dive

Oxlo.ai demonstrates how few-shot learning with large language models enables domain-specific classification without weight updates, using in-context learning to infer patterns from exemplars. The com…

21:35
2026-06-16
dev.to
large-language-models

Comparing LLM Models: A Technical Deep Dive

A developer built a lightweight Python harness to compare production-grade open large language models from Oxlo.ai. The harness sends identical prompts to four models—Llama 3.3 70B, Qwen 3 32B, Kimi K…

19:35
2026-06-16
dev.to
large-language-models

Overcoming LLM Limitations

A developer shipped a small research agent that addresses three common LLM limitations: stale training data, hallucinated facts, and arithmetic errors. The agent uses tool calling to look up facts and…

19:31
2026-06-16
dev.to
large-language-models

Reducing LLM Costs: Best Practices and Techniques

Oxlo.ai offers flat per-request pricing for LLM APIs, decoupling cost from context size and enabling long-context applications without token-based billing. The company provides techniques such as prom…

19:31
2026-06-16
dev.to
large-language-models

The Future of Large Language Models

Oxlo.ai is building an autonomous research agent that converts vague questions into structured plans, gathers evidence across multiple LLM calls, and synthesizes markdown reports. The agent uses small…

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