HISTORY近 30 天历史柱高表示当天去重热搜数量
09/08—10/07 有历史数据
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One week to TechCrunch Disrupt: What’s next for AI and software developmentFirst AI gave us code completion, predicting the lines of code, functions, and boilerplate we needed based on what we’d already typed. Then came code suggestions and code generation, where AI writes new code or improves existing code based on a natural language prompt. Today, coding agents can look at your code, suggest improvements, compile and test the improved code, and iterate on that to come up with something better, faster, and safer. They can plan, write, test, review, and debug code, and - 02
ServiceNow takes aim at enterprise AI’s workflow bottleneck with AI Workflow FactoryServiceNow has launched two AI solutions that can help enterprises identify business processes ripe for automation, build the workflows to address them, and continuously improve them with AI agents. AI Workflow Factory and Autonomous Engineer, announced on Tuesday, will bring process discovery, AI-assisted development and workflow execution into a single system that ServiceNow says can help enterprises move from individual AI projects to continuous workflow improvement. “Instead of starting a ne - 03
Money can’t buy enterprise trustI thought the AI boom was producing a new level of bad behavior , what with MongoDB CEO CJ Desai peacing out, not to mention Google’s earlier controversial “acquihire ” of Windsurf’s executive team. But I was wrong: Money has a long history of hollowing out our ethical norms. The problem in these and other AI moves isn’t really about the money, but rather about the trust, or lack thereof, left in the wake of the moves. My former MongoDB colleague Meghan Gill brought this into focus after Desai’s - 04
Don’t buy a consultant’s AI frameworkEvery major consulting firm offers its own prebuilt architectural framework, model, or reference architecture for generative AI and agentic AI . The pitch is nearly identical from firm to firm: “We’ve done this before, we’ve packaged the patterns, and if you start from our blueprint, you’ll get down the path faster.” It’s an age-old consulting technique. Get the client interested in an out-of-the-box solution, then bill the hours to customize it. The client, as is so often the case, ends up hold - 05
What cloud infrastructure taught me about building billing systemsFor years, I thought of billing as a domain I would have to learn from scratch. Ledgers. Taxes. Proration. Regulations. Money. After 11 years building cloud datacenter management systems, I assumed my infrastructure experience would take me only so far. I was wrong. A few years ago, when I began working on billing, what surprised me most wasn’t how different the domain was. It was how familiar the hardest engineering problems felt. I found myself thinking about the same things I had spent years - 06
Knowing which vulnerability to fix firstCommon Vulnerability Scoring System (CVSS) severity tells you how serious a vulnerability could be. Exploit Prediction Scoring System (EPSS) estimates how likely exploitation is in the next 30 days. Neither one, by itself, tells you what your team should fix first. Run a dependency scan on any moderately sized JavaScript project and you will probably see a table of findings, many of them labeled “high” — 10, 20, sometimes 50 entries. If they all carry the same label, which one gets the next hour - 07
JDK 27: The quiet before the stormEvery September, with metronomic regularity, we have a new version of the Java platform. One small point about timing is that the first release candidate of JDK 27 was delayed by two weeks due to the release of the first Critical Security Patch Update (CPSU) for the JDK. The need for a CPSU is a direct implication of AI models , such as Anthropic’s Claude Mythos, becoming much better at identifying vulnerabilities and developing exploits. If you’re not already re-evaluating your patch strategy f - 08
IBM’s Bob wants to move in: Agent available for on-prem deploymentBob, IBM’s agentic software development platform is now available to run on premises and in private clouds, sovereign clouds, and air-gapped environments to give users more control over their data. IBM is hoping to lure companies operating in heavily regulated environments or managing sensitive source code and regulated data. In such environments, running AI operations means that they are under some restrictions as to where that code can be deployed. That’s a concern for the 68% of executives wh - 09
AI is less dangerous than humansOver the past few weeks, people have been pushing a stack of reports at me as evidence that AI is dangerous to humanity. The OpenAI incident disclosures , the METR and Redwood Research joint investigation, the Nightingale Collective’s DseWiki report, and Ruby Central’s RubyGems update are the latest chapter in a story many people already believe: The machines are turning on us, and this time we have documentation. I understand the appeal. I’ve been working with AI since 1985, and I can tell you - 10
AI could boost software engineer productivity by 32.6%Tools such as Anthropic Claude Code or OpenAI Codex have changed the face of software development, and all the signs are that this investment is set to increase further. But is paying a monthly subscription (and perhaps additional usage fees) cost-effective? Will the increasing level of investment in AI-generated software prove to be worthwhile? That’s a question several economists from the US National Bureau of Economic Research have attempted to answser, at least indirectly. Their paper, snapp - 11
Microsoft, Google back Apache Ossie to make enterprise data and AI platforms more interoperableMicrosoft and Google are joining a project to create an open specification for exchanging semantic models across data, analytics, and AI platforms. The project already has the backing of over 60 companies including Databricks, Informatica, Mistral AI, Nvidia, Oracle, Salesforce, and Snowflake. Their support for Ossie makes it a little more likely that future analytics platforms will be interoperable, but is no guarantee that vendor lock-in will go away, analysts said. The project began life as O - 12
Google makes Gemini 4 AI model available to a trusted fewGoogle has unveiled a new frontier AI model after months of delay. Gemini 4 Argon is designed to handle complex, long-horizon workloads spanning software engineering, enterprise knowledge work such as legal and financial analysis, and cybersecurity. But only a few organizations can get their hands on it for now. Argon is “rolling out to a set of trusted cyber defenders through our Fairwind Program ,” Google wrote in a blog post announcing the release. That limitation, it said, is in order to com - 13
Microsoft doubles down on RustAfter many years of watching how Microsoft develops platforms and rolls out technologies to external users, one thing is clear: anything that is important to Microsoft internally quickly becomes important to anyone building on top of Windows or Azure. The transition from internal to external tooling invariably takes the form of new features in Visual Studio and Visual Studio Code . Often these capabilities have been in use inside Microsoft for years, and they’re ready for you to use immediately - 14
The dream of enterprise semantics: Why this time is differentIn most companies, the struggle to answer a new question fast has nothing to do with a lack of data. The problem is a lack of semantics. Or to put it another way, a lack of agreement about what the data means . Suppose a CFO wants to generate a new report on revenue by customer segment. It sounds simple, but in practice, for the employees tasked with executing the CFO’s directive, it means a multi-day argument about what a “customer segment” even is. Before they can make the report, they need to - 15
OpenAI bets enterprises are ready to delegate real work to autonomous agentsOpenAI says true agentic AI has finally arrived, pushing beyond the trivial capabilities of early-stage virtual assistants. This week at OpenAI Dev Day , the company introduced Dots, which CEO Sam Altman described as “remarkably capable, always-on” agents powered by GPT‑6 Astra. Dots have their own cloud computer, can be accessed in ChatGPT, Slack, or Teams, and plug into more than 4,000 apps based on human-set boundaries. In a keynote, Altman said using it will feel like “a whole new way to wor - 16
Stack Overflow expands Stack Internal to give AI agents ‘trusted’ enterprise knowledgeAs enterprises increasingly turn to coding agents for building applications, Stack Overflow has added new capabilities to Stack Internal, its enterprise-focused platform for centralizing proprietary technical knowledge, to help development teams turn organizational knowledge into trusted, actionable context that these agents can understand and use. Stack Internal now evaluates knowledge based on factors such as provenance, recency, expertise, corroboration and human validation, and then assigns - 17
Unsloth’s model picker had a code-execution problemTrue to its name, AI-model-training tool Unsloth would do more work than it was asked to when developers checked out a model: It would also allow arbitrary code to execute on their machines. Pillar Security found that simply selecting a model in Unsloth Studio caused the application to download and execute Python code from the model repository. This could potentially allow attackers to use a specially crafted model to get malicious code executed on a developer’s system. “The code ran from nothin - 18
Validating AI models and agents with property-based testingTesting deterministic systems is relatively straightforward. Create an assertion that the system should pass, and automate validating it against a series of input-to-output data patterns. When new test patterns are needed, use observability sources to extract them from actual usage data or to create synthetic test data . But testing AI models and agents with nondeterministic outputs doesn’t align with this form of unit testing. For example, say you are using a language model to categorize a stri - 19
Observability for AI-native systems: New SLIs beyond latency and error rateWhen HTTP 200 means nothing An AI assistant can return a response in under a second, maintain 99.9% availability and still give customers a fabricated answer. Traditional dashboards will show green because the request completed successfully. The user, however, received a semantic failure: a response that is syntactically valid but factually wrong, unsafe, biased or irrelevant. That is why AI-native systems need observability beyond latency, error rate, throughput and saturation. LLM applications - 20
AI cuts software developers some slackMy mom used to say that Hodges’s law was “Junk expands to fill the space allotted for it.” We lived in three houses over the years, each one bigger than the previous house as our family grew, and each house became just as full of junk as the one before it. My mom was echoing C. Northcote Parkinson , whose famous law said the same about work—that it expands to fill the time given to get it done. In other words, if you say a task will take a week or two weeks, you’ll be right either way. We all kn