MIT AI Technology Review·
Software engineering has experienced two seismic shifts this century. First was the rise of the open source movement, which gradually made code accessible to developers and engineers everywhere. Second, the adoption of development operations (DevOps) and agile methodologies took software from siloed to collaborative development and from batch to continuous delivery. Now, a third such…
Read full articleInfosys said the integration will be used to help its clients modernize software development, automate workflows and deploy AI systems, initially focusing software engineering, legacy modernization, and DevOps.
The week’s largest round was a $650 million financing for electric pickup truck maker Slate Auto. Other sizable investments went to spaces including drug development, autonomous public transit and software engineering.
Anthropic has today released a new, improved Claude model, Opus 4.7, but has deliberately built it to be less capable than the highly-anticipated Claude Mythos. Anthropic calls Opus 4.7 a “notable improvement” over Opus 4.6, offering advanced software engineering capabilities and improved visioning, memory, instruction-following, and financial analysis. However, the yet-to-be-released (and inadvertently leaked) Mythos seems to overshadow the Opus 4.7 release. Interestingly, Anthropic itself is downplaying Opus 4.7 to an extent, calling it “not as advanced” and “less broadly capable” than the Claude Mythos Preview. The Opus upgrade also comes on the heels of the launch of Project Glasswing, Anthropic’s security initiative that uses Claude Mythos Preview to identify and fix cybersecurity vulnerabilities. “For once in technological history, a product is being released with a marketing message that is focused more on what it does not do than on what it does,” said technology analyst Carmi
Anthropic has today released a new, improved Claude model, Opus 4.7, but has deliberately built it to be less capable than the highly-anticipated Claude Mythos. Anthropic calls Opus 4.7 a “notable improvement” over Opus 4.6, offering advanced software engineering capabilities and improved visioning, memory, instruction-following, and financial analysis. However, the yet-to-be-released (and inadvertently leaked) Mythos seems to overshadow the Opus 4.7 release. Interestingly, Anthropic itself is downplaying Opus 4.7 to an extent, calling it “not as advanced” and “less broadly capable” than the Claude Mythos Preview. The Opus upgrade also comes on the heels of the launch of Project Glasswing, Anthropic’s security initiative that uses Claude Mythos Preview to identify and fix cybersecurity vulnerabilities. “For once in technological history, a product is being released with a marketing message that is focused more on what it does not do than on what it does,” said technology analyst Carmi
Chinese AI company Z.ai has launched GLM-5.1, an open-source coding model it says is built for agentic software engineering. The release comes as AI vendors move beyond autocomplete-style coding tools toward systems that can handle software tasks over longer periods with less human input. Z.ai said GLM-5.1 can sustain performance over hundreds of iterations, an ability it argues sets it apart from models that lose effectiveness in longer sessions. As one example, the company said GLM-5.1 improved a vector database optimization task over more than 600 iterations and 6,000 tool calls, reaching 21,500 queries per second, about six times the best result achieved in a single 50-turn session. In a research note, Z.ai said GLM-5.1 outperformed its predecessor, GLM-5, on several software engineering benchmarks and showed particular strength in repo generation, terminal-based problem solving, and repeated code optimization. The company said the model scored 58.4 on SWE-Bench Pro, compared with
Chinese AI company Z.ai has launched GLM-5.1, an open-source coding model it says is built for agentic software engineering. The release comes as AI vendors move beyond autocomplete-style coding tools toward systems that can handle software tasks over longer periods with less human input. Z.ai said GLM-5.1 can sustain performance over hundreds of iterations, an ability it argues sets it apart from models that lose effectiveness in longer sessions. As one example, the company said GLM-5.1 improved a vector database optimization task over more than 600 iterations and 6,000 tool calls, reaching 21,500 queries per second, about six times the best result achieved in a single 50-turn session. In a research note, Z.ai said GLM-5.1 outperformed its predecessor, GLM-5, on several software engineering benchmarks and showed particular strength in repo generation, terminal-based problem solving, and repeated code optimization. The company said the model scored 58.4 on SWE-Bench Pro, compared with
