From voice to Code(gen)

Andy Bromberg, co-founder of Eco Inc., recorded a short note describing the need to clean up scattered prompts across their app. Within a minute, Codegen launched a run to:

• Scan WhatsApp routes for hardcoded prompts
• Check other surfaces like email, chat, and tools for stray prompts
• Move everything into a centralized LLM config
• Update the code so prompts pull from config instead of being hardcoded

All of it was triggered from a single voice message — no IDE, no manual setup.

Codegen is where you are 

Whether it starts in Slack, Linear, GitHub comments — or even a WhatsApp voice note — Codegen takes the input and turns it into production-ready code. And while Codegen doesn’t yet have a direct WhatsApp voice integration, here’s how such a workflow could work today:

1. Record a note describing requirements (e.g., “Create a React component for user profiles”).
2. A speech-to-text service (like OpenAI Whisper) transcribes the audio.
3. Middleware (n8n, Zapier, or custom API) formats and routes the transcription to Codegen.
4. Codegen analyzes context, generates code, and creates PRs or implementation plans.
5. Codegen’s output is sent back into WhatsApp with code snippets, PR links, or explanations.

Type less, code more

Voice-driven development may sound futuristic, but it’s part of a larger shift already underway: programming is becoming multimodal. Text, chat, voice — they’re all just inputs. What matters is that the AI system can understand your intent, process it in the context of your codebase, and deliver production-ready outputs.

That’s the future we’re building toward at Codegen. Fewer barriers, fewer delays, and workflows that adapt to how developers actually think and work.

See it for yourself — try Codegen for free or schedule a demo to get started.

The post From Voice Notes to Production Code: Yes, You Can Ship from WhatsApp appeared first on The Codegen Blog.

What happens when your project management tool can’t keep up with the AI agents writing and shipping code? The rise of AI agents, code generation, and LLM-augmented workflows are reshaping how software is built — and fast. 

If PM platforms don’t evolve to meet new expectations around automation they risk fading into the background as passive observers in a rapidly accelerating toolchain.

Codegen is built for this moment. It gives PM software companies the power to integrate AI-assisted development, automate high-friction engineering workflows, and deliver value to both developers and non-technical stakeholders. 

Whether you’re building Jira alternatives, internal tools platforms, or verticalized work management software — Codegen enables your team to move faster and differentiate where it counts.

The AI wake-up call for PM platforms

As AI agents become embedded in workflows, project management tools face a new set of challenges. And these pain points aren’t temporary, they’re structural. 

Workflows are fragmenting

Project management tools are no longer the single source of truth. Developers now work across terminal agents, chat interfaces, IDE plugins, and browser-based workflows. A recent report shows 47% of project managers are already using AI, but warns that siloed systems harm productivity unless tools interoperate seamlessly. 

Teams want automation out-of-the-box

Users today demand auto-generated summaries, tracking-aware sprints, and ticket updates without manual input. But only 22% of project managers say AI tools have been deployed and are being used.

PMs tools risk being made redundant 

If your platform doesn’t connect seamlessly with modern tools — it’s friction. AI-native teams want agents that can trigger tasks and deliver context without human babysitting.

Modern teams demand PM platforms that “just work” with tools like GitHub, Slack, or developer agents. Atlassian’s Rovo and other agentic tools emphasize orchestration across systems rather than isolated workflows. Disconnects between planning tools and AI-powered development agents introduce friction — and risk making core PM tools redundant

PM tools become the bottleneck

AI adoption is not just growing — it’s propelling faster shipping cycles. A controlled study with Google engineers found AI-enabled workflows drive ≈ 21% reduction in task completion time on complex tasks. PM platforms must now enable feature delivery that matches AI pace — otherwise their tools become the bottleneck.

How Codegen delivers across 6 critical PM areas

PM platforms must now support dynamic, agentic, and developer-first experiences. Here’s how Codegen delivers across six critical areas for PM enterprise software:

1. Automated feature implementation

In fast-moving product teams, every manual update — whether it’s a task status or a changelog — adds friction. Codegen helps automate the project management glue so teams can stay focused on shipping features, not updating tools.

• Auto-update task statuses based on Git commits and PR merges
• Generate reusable project templates with standardized custom fields
• Provide REST API endpoints for seamless integration with Slack, GitHub, and Jira
• Deliver real-time notifications for milestones and deadlines

2. Agent-driven QA automation

AI-enhanced QA workflows ensure your releases ship with confidence. Codegen can generate and maintain automated tests tailored to user flows, APIs, and edge cases — without relying on manual effort.

• Generate Cypress test suites to validate full user workflows
• Automate API testing to ensure data consistency and integrity
• Run cross-browser tests to guarantee a consistent UX
• Conduct load testing on collaboration-heavy features

3. Real-time dev insights for PMs

Product managers need visibility without slowing teams down. With Codegen, PM software platforms can surface live engineering metrics, velocity trends, and predictive signals without constant check-ins.

• Build real-time velocity dashboards that show sprint progress and blockers
• Track code quality through coverage and technical debt analysis
• Monitor resource utilization to support better planning and staffing
• Use predictive models to forecast delivery timelines

4. Faster integration development

Shipping a great PM experience means connecting your software to the broader enterprise toolchain. Codegen accelerates integration delivery so your platform doesn’t get left behind.

• Build SDKs for key partners like Salesforce, HubSpot, and Microsoft Teams
• Implement webhook systems for real-time data sync
• Streamline OAuth flows with secure token handling
• Create unified API gateways with rate limiting and authentication

5. Standardize code across modules

In large codebases, inconsistency breeds regressions. Codegen helps enforce best practices automatically — making scalable development the default, not the exception.

• Generate reusable UI libraries for consistent product design
• Enforce linting and formatting with ESLint and Prettier pre-commit hooks
• Establish shared patterns for API calls and state management
• Auto-generate documentation for faster onboarding and handoffs

6. On-demand developer productivity

Developer time is your most valuable resource. With Codegen, you can reduce boilerplate, speed up onboarding, and automate the repetitive — but essential — parts of building and scaling.

• Use CLI tools to generate common boilerplate (CRUD, APIs, components)
• Refactor codebases at scale (e.g., class to hooks migration)
• Automate dev environment setup with Docker configurations
• Perform code reviews focused on security, performance, and style

Ready to accelerate?

PM tools need intelligent systems that build, test, and adapt with your team. Codegen delivers that edge. Turn your specs into shippable code, automate the grunt work, and give your engineers the power to move faster with confidence.

If you’re building for scale and speed, it’s time to level up your stack.

Set up a demo to get started with Codegen today!

The post Why Top PM Tools Are Leveling Up with Codegen appeared first on The Codegen Blog.

Rather than thinking in tickets, sprints, or even lines of code, developers are now thinking in intent. And intent, when paired with a capable agent, is actionable.

From Copilots to Autonomous Collaborators

AI tools started as passive assistants — autocomplete on steroids. GitHub Copilot showed us how LLMs could reduce keystrokes, improve syntax accuracy, and cut boilerplate. But today’s agents go further: they manage state, orchestrate tools, and carry context across multi-step tasks.

Some refer to these as goal-driven agents — tools that perceive context, make decisions, and complete workflows. Companies like GitLab are embedding them deeply, transforming dev environments into adaptive systems that evolve with developer behavior.

Take Anthropic’s Claude as a benchmark. At its first Developer Day, Anthropic revealed that Claude contributed to over 70% of pull requests during internal rebuilds. This isn’t just efficiency — it’s intent realization. A developer thinks it, prompts it, and an agent executes it. The engineer becomes a director of systems, not just an executor of logic.

Productivity Is Real, But So Is the Shift in Mental Models

The data is compelling: developers using Copilot complete tasks over 55% faster than those who don’t, according to a controlled GitHub study. Anecdotal reports from companies like ZoomInfo and Shopify mirror that acceleration, with engineers embracing AI not just as a tool — but as a workflow layer.

But what’s more important than speed is where that speed comes from: fewer context switches, lower cognitive load, and faster traversal from ambiguity to code. AI is effectively compressing the space between intent and implementation.

This is why engineers using agentic tooling report a shift in how they work. They don’t just write code — they:

• Prompt systems to generate architectural scaffolding
• Review AI-generated PRs before pushing them to prod
• Use Slack-integrated bots to summarize commits or update documentation
• Orchestrate multi-repo changes with natural language

This isn’t automation in the traditional sense — it’s delegation. Engineers hand off well-scoped goals and shift their attention to the creative and strategic layers of their work.

Enterprise Is Leaning In

Large organizations aren’t sitting on the sidelines. At Microsoft Build 2025, CTO Kevin Scott announced that AI agent usage had doubled year over year. Teams are integrating Copilot into infrastructure, documentation, and even support ticket resolution. 

Anthropic, meanwhile, positions Claude not as a chatbot but as an engineering collaborator. They advertise that Claude can persist context across sessions, helping teams tackle deeply nested codebases or long-form technical planning.

This institutional shift is not about novelty — it’s about scaling human capability without scaling headcount. When AI agents can handle documentation, testing scaffolds, or release notes, engineering teams become more agile, more focused, and more aligned with business goals.

Redefining Developer Workflows

We’re not just seeing new tools — we’re witnessing a new developer archetype: someone who designs systems by intent, verifies the results, and iterates through natural feedback loops.

A modern, AI-augmented workflow might look like this:

• A developer prompts a Slack-integrated agent to scaffold a new API route.
• The agent sets up the logic, generates a PR, and pushes to a staging branch.
• A second agent reviews for style, test coverage, and data compliance.
• The developer verifies the diff, merges, and triggers a changelog generator.
• Everything — from docs to deployment — is coordinated, not coded from scratch.

The result? More time for strategy, architecture, and collaboration. Less time chasing formatting, environment setup, or repetitive logic.

Risks and Responsibilities

Of course, the shift to agentic workflows introduces new responsibilities. GitHub CEO Thomas Dohmke recently warned developers against “vibe coding” aka blindly trusting LLM output without verification. And rightly so: more abstraction means more reliance on systems developers must learn to trust and guide.

That’s why the most successful teams don’t just adopt AI — they instrument it. They measure how often AI suggestions are used, where agents reduce PR cycle time, and which types of tasks should not be delegated. Like any good system, trust is earned through reliability, transparency, and iteration.

Codegen’s Bet: You Should Orchestrate, Not Just Code

At Codegen, we believe developers shouldn’t be stuck stitching boilerplate together. You should be guiding systems — refining intent, iterating outputs, and spending your energy where it matters: building great products.

That’s why we’re designing agents that live in your real workflows. You can describe what you want in Slack, generate PRs, refine features, fix bugs, or roll out a change to a million-line codebase — all through conversation. These aren’t isolated tools. They’re collaborators that understand context and help you move faster with confidence.

The Path Ahead

The AI era isn’t about replacing developers — it’s about reimagining what developers can achieve when given better leverage. With AI agents, engineering shifts from being reactive to strategic. From sprinting to orchestrating. From manual to intelligent.

If you’re building software in 2025, you’re building it with AI — whether you’ve realized it yet or not. And if you’re using Codegen, you’re doing it with an agent that understands your codebase, your workflow, and your intent.

Welcome to the new developer workflow. We’re just getting started.

The post Reimagining Developer Workflows for the AI Era appeared first on The Codegen Blog.

What’s Possible Today

Here are real examples of what teams are accomplishing with Codegen right now:

Development & Testing

• “Migrate me from Jest to Vitest” – Automatically update test frameworks across your entire codebase
• “Find all the TypeScript errors in our codebase, make a ticket for each one, then solve it” – Comprehensive error detection and resolution
• “Upgrade our React components to use hooks instead of class components” – Modernize legacy codebases automatically

Project Management Integration

• “Convert these Notion meeting notes to Linear tickets for follow-up, then do them” – Seamless workflow from planning to execution
• “Create a comprehensive project roadmap from our GitHub issues and update our team dashboard” – Intelligent project coordination

Documentation & Knowledge Management

• “Create me a knowledge base in Notion from my codebase” – Automatically generate comprehensive documentation
• “Generate API documentation and keep it synced with code changes” – Living documentation that never goes stale

Data & Analytics

• “Tell me about my D7 retention based on our database” – Instant insights from your data warehouse
• “Create a dashboard showing our most critical performance metrics and set up alerts” – Proactive monitoring and visualization

DevOps & Infrastructure

• “Set up CI/CD pipeline with proper testing and deployment stages” – Infrastructure as conversation
• “Audit our security practices and implement recommended fixes” – Comprehensive security improvements

Beyond Code Generation

Codegen isn’t just about writing code—it’s about orchestrating your entire development ecosystem. From GitHub and Linear to Notion and Slack, Codegen connects the dots between your tools, turning scattered workflows into seamless automation.

The future of software development isn’t about replacing developers—it’s about amplifying their capabilities. With Codegen, your team can focus on what matters most: building great products.

Ready to Ship with AI?

Experience the future of development today. Visit codegen.com to get started.

The post Ship with AI: The Future of Coding is Here appeared first on The Codegen Blog.

Yet there’s a striking gap between understanding and action for today’s code assistants. When provided proper context, frontier LLMs can analyze massive enterprise codebases and propose practical paths towards sophisticated, large-scale improvements. But implementing changes that impact more than a small set of files with modern AI assistants is fundamentally infeasible. The good news is that for focused, file-level changes, we’ve found real success: AI-powered IDEs (Windsurf, Cursor) are transforming how developers write and review code, while chat-based assistants are revolutionizing how we bootstrap and prototype new applications (via tools like v0, lovable.dev, and bolt.new).

However, there’s a whole class of critical engineering tasks that remain out of reach—tasks that are fundamentally programmatic and deal with codebase structure at scale. Much of modern engineering effort is directed towards eliminating tech debt, managing migrations, analyzing dependency graphs, enforcing type coverage, and other global concerns. Today’s AI assistants can propose solutions but lack the mechanisms to execute them. The intelligence is there, but it’s trapped in your IDE’s text completion window.

The bottleneck isn’t intelligence—it’s tooling. The solution is giving AI systems the ability to programmatically interact with codebases through code execution environments. These environments are the most expressive tools we can offer agents, enabling composition, abstraction, and systematic manipulation of complex systems. By combining code execution with custom APIs for large-scale operations, we unlock new high-value use cases.

Beating Minecraft with Code Execution

In mid-2023, a research project called Voyager solved Minecraft, performing several multiples better than prior SOTA. This success wasn’t about raw intelligence—it was about providing a more expressive action space: code.

GPT-4, when allowed to write and execute JavaScript programs through a clean API, could craft high-level behaviors and reuse learned “action programs” across tasks. This enabled skill accumulation, experience recall, and systematic reuse.

“We opt to use code as the action space instead of low-level motor commands because programs can naturally represent temporally extended and compositional actions…”

Code is an Ideal Action Space

Letting AI act through code rather than atomic commands yields a step change in capability. In software engineering, this means expressing assistant behavior through code that manipulates codebases.

# Implement `grep` via for loops and if statements
for function in codebase.functions:
    if 'Page' in function.name:
        function.move_to_file('/pages/' + function.name + '.tsx')

This paradigm brings multiple advantages:

• API-Driven Extensibility: Agents can use any operation exposed via a clean API.
• Programmatic Efficiency: Batch operations across large codebases are fast and systematic.
• Composability: Agents can chain simple operations to form more complex ones.
• Constrained Action Space: APIs act as guardrails, preventing invalid actions.
• Objective Feedback: Errors provide clear debugging signals.
• Natural Collaboration: Code is human-readable and reviewable.

Code Manipulation Programs

To match how developers think about code, agents need high-level APIs, not raw AST surgery. We’re building a framework that reflects actual engineering intuition and abstracts over common edge cases, while preserving correctness.

# Access to high-level semantic operations
for component in codebase.jsx_components:
    if len(component.usages) == 0:
        component.rename(component.name + 'Page')

This isn’t string substitution. The framework understands structure: React hierarchies, type systems, usage graphs. It enables both rapid analysis and safe edits.

We’re also extending this interface to systems outside the repo: AWS, Datadog, and CI/CD platforms. This is the path to autonomous software engineering.

Codegen is now OSS

We’re excited to release Codegen as open source under Apache 2.0 and build out this vision with the developer community. Schedule a demo or join our Slack community to share ideas and feedback.

— Jay Hack, Founder

The post Act via Code appeared first on The Codegen Blog.