Claude Blog 采集 (2026-05-26)¶
共采集 5 篇文章
📋 文章索引¶
- How AI helps break the cost barrier to COBOL modernization - Feb 23, 2026 (评分: 9.5)
- Harnessing Claude’s intelligence - Apr 02, 2026 (评分: 9.0)
- How a non-technical project manager built and shipped a stress management app with Claude Code in six weeks - May 01, 2026 (评分: 9.0)
- Fix software bugs faster with Claude - Oct 28, 2025 (评分: 7.9)
- How an Anthropic sales leader uses Claude Cowork to run a 4,000-account book - May 20, 2026 (评分: 7.4)
How AI helps break the cost barrier to COBOL modernization¶
来源: Claude Blog 发布日期: Feb 23, 2026 采集时间: 2026-05-26 价值评分: 9.5/10 正文字数: ~6826 字符
摘要¶
The economics of COBOL modernization have shifted. AI makes the economics work by automating what used to require armies of consultants.
正文内容¶
Legacy code modernization stalled for years because understanding legacy code cost more than rewriting it. AI flips that equation.
COBOL is everywhere. It handles an estimated 95% of ATM transactions in the US . Hundreds of billions of lines of COBOL run in production every day, powering critical systems in finance, airlines, and government. Despite that, the number of people who understand it shrinks every year. The developers who built these systems retired years ago, and the institutional knowledge they carried left with them. Production code has been modified repeatedly over decades, but the documentation hasn't kept up. Meanwhile, we aren't exactly minting replacements—COBOL is taught at only a handful of universities, and finding engineers who can read it gets harder every quarter. Given these roadblocks, how can organizations modernize their systems without losing the reliability, availability, and data they’ve accumulated over decades? And without breaking anything? Why modernizing COBOL is different COBOL modernization differs fundamentally from typical legacy code refactoring. You aren’t just updating familiar code to use better patterns, you’re reverse engineering business logic from systems built when Nixon was president. You’re untangling dependencies that evolved over decades, and translating institutional knowledge that now exists only in the code itself. Modernizing a COBOL system once required armies of consultants spending years mapping workflows. This resulted in large timelines and high costs that few were willing to take on. AI changes this. Tools like Claude Code can automate the exploration and analysis phases that consume most of the effort in COBOL modernization. These tools can: Map dependencies across thousands of lines of code Document workflows that nobody remembers Identify risks that would take human analysts months to surface Provide teams with the deep insights they need to make informed decisions With AI, teams can modernize their COBOL codebase in quarters instead of years. How AI changes COBOL modernization AI excels at streamlining the tasks that once made COBOL modernization cost-prohibitive. With it, your team can focus on strategy, risk assessment, and business logic while AI automates the code analysis and implementation. Automated exploration and discovery AI starts by reading your entire COBOL codebase and mapping the structure. It identifies program entry points, traces execution paths through called subroutines, maps data flows between modules, and documents dependencies that span hundreds of files. This kind of mapping goes beyond simple call graphs. Shared data structures, file operations that create coupling between modules, initialization sequences that affect runtime behavior—these implicit dependencies don't show up in static analysis because they involve data shared through files, databases, or global state. They're also exactly what makes COBOL modernization risky, which is why automated discovery matters: it finds these hidden relationships before they cause problems during migration. Workflow documentation also emerges out of this analysis. By tracing how data moves through a system from input to output, AI can produce diagrams and written descriptions of processing pipelines that nobody remembers building but everyone depends on. Risk analysis and opportunity mapping With the codebase mapped, AI can assess which components are safe to move and which need careful handling. Modules with high coupling can be more risky to modernize. Isolated components surface as candidates for early, independent modernization. Duplicated logic points to refactoring opportunities. Areas with accumulated technical debt get documented before they become migration surprises. Strategic planning with expert oversight This is where human judgment becomes essential. Your COBOL engineers bring the understanding of regulatory requirements, business priorities, operational constraints, and risk tolerance that AI cannot. The planning phase develops a detailed roadmap that sequences modernization work strategically: AI suggests prioritization based on the risks, dependencies, and complexity it identified during analysis. Your team reviews these recommendations and decides which components to modernize first based on business value, technical risk, and organizational priorities. This is also when your team defines the target architecture, code standards, and integration requirements for modernized components. Code testing and validation are also defined before any code changes: AI designs preliminary function tests that verify migrated code produces identical outputs to legacy COBOL. Your team decides whether those tests are sufficient, which business scenarios need manual validation by subject-matter experts, and what performance benchmarks the modernized components need to meet. Incremental implementation with continuous validation Execution happens one component at a time, with validation at each step. AI translates COBOL logic into modern languages, creates API wrappers around legacy components that stay in place, and builds the scaffolding to run old and new code side by side during transition. Each step either succeeds and gets validated, or fails and gets corrected while the scope is small. You never have massive changes in flight where failure means rolling back weeks of work. As your team sees modernized components passing tests, they gain confidence to tackle progressively more complex parts of the system. Start your COBOL modernization The approach outlined above works for COBOL systems of any size. Tools like Claude Code can automate much of the exploration and analysis work described, giving your team the comprehensive understanding they need to plan and execute migrations confidently. Start with a single component or workflow that has clear boundaries and moderate complexity. Use AI to analyze and document it thoroughly, plan the modernization with your engineers, implement incrementally with testing at each step, and validate carefully. This will build organizational confidence and surface adjustments needed for your systems. The economics of COBOL modernization have shifted. AI makes the economics work by automating what used to require armies of consultants, freeing your engineers to make the migration decisions that require their domain expertise. For a step-by-step guide, see the Code Modernization Playbook .
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采集自 Claude Blog，由 collect_claude_blog.py 自动采集
Harnessing Claude’s intelligence¶
来源: Claude Blog 发布日期: Apr 02, 2026 采集时间: 2026-05-26 价值评分: 9.0/10 正文字数: ~1657 字符
摘要¶
Three patterns for building on the Claude Platform that keep pace with Claude's evolving intelligence while balancing latency and cost.
正文内容¶
Building applications that balance intelligence, latency, and cost.
One of Anthropic’s co-founders, Chris Olah, says that generative AI systems like Claude are grown more than they are built. Researchers set the conditions to direct growth, but the exact structure or capabilities that emerge aren’t always predictable. This creates a challenge for building with Claude: agent harnesses encode assumptions about what Claude can’t do on its own, but those assumptions grow stale as Claude gets more capable. Even lessons shared in articles like this deserve frequent revisiting. In this article, we share three patterns that teams should use when building applications that keep pace with Claude’s evolving intelligence while balancing latency and cost: use what it already knows, ask what you can stop doing, and carefully set boundaries with the agent harness. 1. Use what Claude knows We suggest building applications using tools that Claude understands well. In late 2024, Claude 3.5 Sonnet reached 49% on SWE-bench Verified—then state of the art —with only a bash tool and a text editor tool for viewing, creating, and editing files. Claude Code is grounded in these same tools. Bash wasn’t designed for building agents, but it's a tool that Claude knows how to use and gets better at using over time.
Explore more product news and best practices for teams building with Claude.
Transform how your organization operates with Claude
Product updates, how-tos, community spotlights, and more. Delivered monthly to your inbox.
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采集自 Claude Blog，由 collect_claude_blog.py 自动采集
How a non-technical project manager built and shipped a stress management app with Claude Code in six weeks¶
来源: Claude Blog 发布日期: May 01, 2026 采集时间: 2026-05-26 价值评分: 9.0/10 正文字数: ~814 字符
摘要¶
Kostiantyn Vlasenko had never written a line of code when he set out to build Respiro. Just over a month later, his product was live on the App Store.
正文内容¶
Kostiantyn Vlasenko had never written a line of code when he set out to build Respiro. Just over a month later, his product was live on the App Store.
In our series, Day zero: founder stories , we profile the builders behind some of the world’s most interesting and inspiring startups. In this article, we highlight Kostiantyn Vlasenko, a winner of the Claude Opus 4.6 Hackathon and creator of Respiro. Founder Kostiantyn Vlasenko Country Ukraine Startup Respiro
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Transform how your organization operates with Claude
Product updates, how-tos, community spotlights, and more. Delivered monthly to your inbox.
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采集自 Claude Blog，由 collect_claude_blog.py 自动采集
Fix software bugs faster with Claude¶
来源: Claude Blog 发布日期: Oct 28, 2025 采集时间: 2026-05-26 价值评分: 7.9/10 正文字数: ~6476 字符
摘要¶
Turn debugging from detective work into systematic problem-solving. Analyze errors, trace root causes, and implement fixes faster. Debugging code is time-consuming and tedious. And the hardest part isn't usually fixing the bug itself; it’s understanding why your code broke in the first place.
正文内容¶
Turn debugging from detective work into systematic problem-solving. Analyze errors, trace root causes, and implement fixes faster.
Debugging code is time-consuming and tedious. And the hardest part isn't usually fixing the bug itself; it’s understanding why your code broke in the first place. Your test suite fails, but the error message points to a symptom, not the cause. A user reports unexpected behavior that traces back to code from three sprints ago. The real issue might be hiding in some dependency you forgot existed. Each bug pulls you out of flow state into detective mode, hunting through logs and stack traces when you'd rather be building. Most teams debug the same way: dig through logs, reproduce locally, add more logging, then manually trace through recent changes. These methods work, but they're slow. Each step requires deep context about your system, and correlation work that stretches a 20-minute fix into a 3-hour investigation. Here's how to turn debugging from detective work into systematic problem-solving. How most debugging actually happens Parse the logs, trace the error The debugging process typically begins by examining application logs or stack traces. Using tools like Splunk or ELK, you correlate error messages across services, follow request flows, and piece together what happened during the failure window. This works when error messages clearly point to the problematic code and your logs have enough business context, but production systems generate massive log volumes that need domain expertise to parse. Stack traces show where code failed, not why validation rules triggered or what external services returned. Reproduce the issue locally Next, you create controlled test scenarios to recreate the production problem locally, where you can use breakpoints and step-through debugging. This means building reproduction setups with specific data and simulated user interactions. Local reproduction gives you visibility into code execution, but many production issues only happen under specific conditions. Combinations of system load, third-party behavior, and real user data that staging environments can't replicate. Performance problems and race conditions behave completely differently under artificial test conditions. Add instrumentation, deploy, wait When logs aren't enough, add logging statements around suspected problem areas, deploy observability improvements, and capture more granular system behavior during failures. You'll gain valuable insights, but it requires production deployments that introduce risk and extend timelines. Debug logging impacts performance in high-traffic environments. The cycle of adding instrumentation, deploying, and waiting for reproduction often stretches debugging from hours to days. Review recent code changes You review recent commits, dependency updates, and configuration changes that coincided with issue emergence. This involves digging through Git histories, pull request discussions, and deployment logs to identify problematic modifications. Thorough analysis can identify root causes, but modern deployment means dozens of changes reach production daily across multiple services. Complex issues span repositories and require understanding interactions between seemingly unrelated modifications. Collaborate on bug analysis with Claude Developers of all skill levels can integrate AI coding assistants like Claude into their debugging workflows for immediate error analysis. You can collaborate with Claude to debug in two different ways: Claude.ai : Free web interface. Paste stack traces, describe bugs, get quick analysis with specific hypotheses and investigation paths. Any browser, desktop, or mobile. Claude Code : Command line tool for agentic coding . Unlike traditional assistants that wait for your next instruction, Claude Code autonomously works through multi-step debugging workflows reading error traces, analyzing code across files, running diagnostic tests, and implementing fixes while you focus on other tasks. Start with Claude.ai Before diving deep into your codebase, use Claude.ai to quickly analyze error patterns and generate investigation hypotheses. The web interface lets you paste stack traces, describe symptoms, and get immediate feedback. This first-pass analysis helps you understand what to look for before committing to time-intensive debugging approaches. Common debugging questions developers ask Claude: "Here's a test failure from CI. What could be causing it?" "Why might this Redux selector return undefined sometimes?" "Compare our debounce and throttle helpers—which is safer?" These questions help you quickly validate theories, identify blind spots in your investigation, and prioritize which debugging approaches to try first. Identify failure modes early Before diving into code, Claude helps you think through potential issues systematically. Ask Claude to identify scenarios that trigger specific errors: timeouts, rate limiting, missing fields. Example : "What could cause pagination to silently drop results in this API call?" Claude outlines common culprits like cursor mishandling, connection limits, race conditions. Get a focused set of issues to investigate instead of hunting blindly. Transform logs into action items Paste cryptic error logs into Claude. Ask for "probable root causes ranked by likelihood." Claude identifies patterns in error data, highlighting the specific service, configuration change, or code path that's responsible. Instead of "investigate API failures," your team gets "check rate limiting in auth service." Scale up with Claude Code for complex investigations When bugs span your entire codebase, Claude Code acts as an autonomous debugging partner. Unlike traditional coding assistants that wait for instructions, Claude Code can independently explore your project, following debugging trails across files, and executing the investigative workflow a seasoned developer would take, all while you focus on other tasks. Install: npm install -g @anthropic-ai/claude-code Launch in your project: claude Then immediately start investigating:
Explore more product news and best practices for teams building with Claude.
Transform how your organization operates with Claude
Product updates, how-tos, community spotlights, and more. Delivered monthly to your inbox.
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采集自 Claude Blog，由 collect_claude_blog.py 自动采集
How an Anthropic sales leader uses Claude Cowork to run a 4,000-account book¶
来源: Claude Blog 发布日期: May 20, 2026 采集时间: 2026-05-26 价值评分: 7.4/10 正文字数: ~4981 字符
摘要¶
Travis Bryant, Head of US Mid-Market GTM at Anthropic, shares how he uses Claude Cowork to prepare customer briefs and weekly forecasts, and to run an overnight territory scoring that used to take cross-functional teams hundreds of hours.
正文内容¶
Travis Bryant, Head of US Mid-Market GTM at Anthropic, shares how he uses Claude Cowork to prepare customer briefs and weekly forecasts, and to run an overnight territory scoring that used to take cross-functional teams hundreds of hours. Travis walks through how he uses Claude Cowork to prepare customer briefs, pull weekly forecasts, and score 4,000 accounts overnight.
In sales leadership, the job is to make judgment calls: where to invest the team's hours, and what to tell leadership about how the quarter is shaping up. The work surrounding those decisions, like assembling data from four systems and re-baselining numbers every time numbers refresh, used to eat most of my week. Claude Cowork has shifted that balance: it handles the data assembly and reformatting, so I can dedicate more time to customer conversations and strategic decisions. I run US mid-market go-to-market at Anthropic, which means I'm responsible for 4,000 accounts split between mid-market tech and industries. Mid-market includes tech companies that aren't startups anymore, but aren't large enterprises yet either. Industries cover everything else, from financial services and healthcare to retail and manufacturing. My work spans three cadences: daily customer call prep, the weekly forecast rollup to Anthropic's sales leadership, and quarterly territory and prospect-list work across the full 4,000-account book. Each is a lot of work on its own and doing all three well used to be impossible. I tried Claude Code, but never got comfortable with working with the terminal. Claude Cowork wraps the same engine in an interface I can work in. That was when it clicked: I finally had a way to hand off the work and trust it would get done. What I run in Claude Cowork every day, week, and quarter Most of the time I save with Claude Cowork falls into about 90 minutes of small daily wins, or micro-optimizations, that it runs for me. Each morning, a scheduled task runs a skill that scans my Google Calendar and books a conference room for any external meeting that's missing one. A second skill runs customer call prep before each meeting, pulling spend from BigQuery and pipeline status from Salesforce into a brief that's waiting for me when I open my laptop. The scheduler was the bigger unlock than the skill itself. Once prep stops being a slash command I have to remember and starts running on its own, I stop forgetting it. The Friday forecast is the next tier up and it saves me about three hours per week. A scheduled skill pulls opportunity records and submitted commits from Salesforce's Forecast tab, token spend from BigQuery, and notes from a handful of internal documents. It assembles a single-page web report in the exact format Anthropic's sales leadership wants to read: top-line metrics, top deals, movers and decliners, and the forecast snapshot rolled up from each first-line manager. The skill deploys the page to an internally shared link before Monday's forecast call. My Monday job is to add commentary: Claude builds the what; I do the why. The biggest project I've run through Claude Cowork was account propensity scoring for the whole mid-market segment. Every fiscal year, every account in the book needs a score that helps the assigned AE prioritize the territory. In previous companies and roles, work like this ran for hundreds of hours across RevOps, FP&A, and marketing. I did it in one night. I started by defining two five-dimension scoring rubrics with Claude: one for tech accounts and one for industries. The dimensions for tech were agent opportunity, internal transformation, AI commitment, white space against existing spend, and industry fit. Industries used a different rubric, with dimensions like knowledge-worker density (which can be very high at law firms, for example, and lower in manufacturing because so many employees are on the shop floor), and public AI commitments measured by mentions on the company's open jobs page. Once the rubric was set, I pointed Claude Cowork at the 4,000-account list. Claude Cowork ran overnight, scoring each account one by one with deep web research, Salesforce data, and BigQuery data, producing a numerical score and a written rationale for every dimension. Then I asked Claude Cowork to build an interactive dashboard from the results. Each AE clicks into their territory's pie slice and sees their accounts ranked by score, with the rationale generated for each dimension. Hovering over an account surfaces potential use cases and comparable case studies for prospecting. The dashboard turned the scores from a data exercise into a working sales tool.
Explore more product news and best practices for teams building with Claude.
Transform how your organization operates with Claude
Product updates, how-tos, community spotlights, and more. Delivered monthly to your inbox.
Please provide your email address if you'd like to receive our monthly developer newsletter. You can unsubscribe at any time.
采集自 Claude Blog，由 collect_claude_blog.py 自动采集