Z.ai Launches GLM-5.2 With a Usable 1M-Token Context, Two Thinking-Effort Levels, and No Benchmarks at Launch

GLM-5.2 is the latest large language model from Z.ai, becoming the third major release in the GLM-5 line. It follows GLM-5 (February 11), GLM-5-Turbo (March 15), and GLM-5.1 (April 7). That makes four flagship-tier coding releases in roughly four months. Usable 1M-Token Context Window GLM-5.2’s standout spec is a 1,000,000-token context window. Z.ai labels the…

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Orbio raises  million to automate hiring and onboarding for frontline employees

Orbio raises $21 million to automate hiring and onboarding for frontline employees

After Sergi Bastardas’ decade at Amazon and floriculture startup Colvin, one factor at all times stood out  — the sensation that there wasn’t sufficient environment friendly “human infrastructure” to handle the employees behind the scenes. He took this sense and, in 2025, alongside his co-founders Nacho Travesí and Antonio Melé, launched Orbio, an enterprise startup…

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Claude Code Information 2026: 25 Options with Examples + Demo

Claude Code Information 2026: 25 Options with Examples + Demo

Interactive Demo Claude Code: 25 Options Explorer & Playground Discover the characteristic stack, run a simulated terminal, check Auto Mode, and construct a CLAUDE.md. Runs totally in your browser. All 25 Official Method Third-party Select a characteristic claude-code — simulated > Attempt: /assist /init /context /evaluate /security-review /mcp /brokers /compact — or kind a plain…

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A Coding Fingers-On on FineWeb for Streaming, Filtering, Deduplication, Tokenization, and Giant-Scale Net Corpus Analytics

A Coding Fingers-On on FineWeb for Streaming, Filtering, Deduplication, Tokenization, and Giant-Scale Net Corpus Analytics

df[“domain”] = df[“url”].apply(lambda u: urlparse(u).netloc.substitute(“www.”, “”) if isinstance(u, str) else “?”) top_domains = df[“domain”].value_counts().head(15) print(“n— High 15 domains in pattern —“) print(top_domains) fig, axes = plt.subplots(2, 2, figsize=(14, 10)) axes[0, 0].hist(df[“token_count”].clip(higher=4000), bins=50, shade=”#7b2d26″) axes[0, 0].set_title(“Token depend per doc (gpt2)”) axes[0, 0].set_xlabel(“tokens”); axes[0, 0].set_ylabel(“docs”) axes[0, 1].hist(df[“language_score”], bins=40, shade=”#2d5d7b”) axes[0, 1].axvline(0.65, shade=”pink”, ls=”–“, label=”FineWeb cutoff 0.65”) axes[0,…

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