404 summarised stories about AI Coding Agents, each linking back to the original source. Browse all topics →
SiliconANGLE 2 months ago 12 ● 8 sources
Google launched Gemini 3.7 Flash, its most capable entry-level model for coding and AI agent projects. It is rolling out 3 weeks after Gemini 3.6 Flash and is priced at half the cost of Gemini 3.6 Flash through the end of the year. Developers get improved coding and UI generation plus added ability to power AI agent ensembles and to browse/use Google services via Gemini Spark.
MarkTechPost 2 months ago 12 ● 8 sources
Google released Gemini 3.7 Flash, a refined update in its Flash tier, for multi-modal input and agent-style use via hosted APIs. It costs $0.75 per 1M input tokens (and $3.75 per 1M output tokens). Pricing drops and deployability remains API/enterprise-only, with reported gains on coding and document-work benchmarks alongside some reasoning regressions.
Google 2 months ago 39 ● 8 sources
Google introduced Gemini 3.7 Flash, a coding-and-agent model positioned as a more intelligent “workhorse” than its Gemini 3.6 Flash predecessor. It launched three weeks after 3.6 Flash and is priced at $0.75 per 1M input tokens and $3.75 per 1M output tokens. The update improves coding accuracy on benchmarks, web/app generation in fewer prompts, and knowledge-work reasoning, while being rolled into Gemini Spark and shipping with updated safety safeguards.
Deep Learning Weekly 2 months ago 37
Deep Learning Weekly compiled this week’s deep-learning updates, including AI model releases, agent and evaluation tooling, and new research papers. The BDH-CQ paper reports a 150M-parameter setup achieving 29.5% pass@2 at a computed inference cost of $0.0007 per task. The roundup changes by giving readers a curated set of concrete releases, benchmarks, and measurements to track rather than any single narrative development.
Product Hunt 2 months ago 11 ● 8 sources
Gemini 3.7 Flash is presented as Google’s workhorse model for coding and agent tasks. No number, benchmark, or date is mentioned in the provided text. As a result, Google is positioning a new Gemini version for developer-oriented AI coding and agent use cases.
codewithbullet.com 2 months ago 45
Bullet, a coding agent from founders Adi and Alex, was launched after six pivots focused on reducing delays from earlier coding agents like Claude Code and Codex. On SWE-bench Verified, Bullet resolved 479/500 tasks (95.8%) in one attempt, averaging 119s per task. It changes agent behavior by using model routing, faster code+context search, bounded context, and more efficient turn-taking to cut round trips and lower cost.
The Batch 43 ● 4 sources
DeepSeek released DeepSeek-V4-Flash-0731, a fine-tuned small model that overtook its own DeepSeek-V4-Pro on independent tests. It scored 50 points on Artificial Analysis’ Intelligence Index (max reasoning), up from 44 for DeepSeek-V4-Pro. The update improves agentic performance and lowers per-task costs through the unchanged V4-Flash architecture with new fine-tuning, with weights available under the MIT license and API prices starting at $0.14 per 1 million input tokens.
Tech Funding News 2 months ago 24 ● 3 sources
CodeRabbit raised a Series C and expanded its AI code review product into Agentic Change Management. It secured $143 million at a $1.5 billion valuation. The company added Triage, Change Stack, and CodeRabbit Security and is scaling its European and Asian operations while keeping AI review tools free for open-source maintainers.
Product Hunt 2 months ago 35
The post discusses running Claude Code and Codex-style coding tools on a phone. No date, price, or numeric benchmark is provided in the article text. As a result, the focus is on mobile use of AI coding tools rather than reporting any measured performance or release details.
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25 October 2026
The biggest AI story today isn’t a model release—it’s the map. As the US and China harden into rival AI blocs, “swing countries” are trying to keep options open, and Kazakhstan’s moves show how fast the chessboard is shifting. After trips and talks on both sides, it signed an investment deal worth up to $10 billion with Nvidia for a “data center valley,” while still engaging with Chinese frameworks via initiatives like China’s World AI Cooperation Organization. The underlying point is blunt: when chip access, open-source choices, and procurement rules tighten, governments can’t play both sides indefinitely—and they’re already rebalancing around what they can plausibly run.
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