12 posts tagged with "AI"

AI coding assistants have evolved from simple autocompletion tools into sophisticated development partners. Claude Code represents the next step in this evolution, offering a framework for what can be described as "autonomous programming." It's a tool designed to integrate deeply into your workflow, do jobs what AI coding previously cannot do:

• Code Understanding & Q&A: Acts as a project expert, explaining how large codebases work, making it invaluable for onboarding new team members.
• Large-Scale Refactoring: Excels at modifying massive files (e.g., 18,000+ lines) where other AIs fail, thanks to its ability to understand global code relationships.
• Debugging: Provides step-by-step reasoning to find the root cause of bugs, unlike tools that just offer a fix without context.
• Complex Feature Generation: Follows an "explore → plan → implement" workflow. It can be prompted to first analyze the problem and create a detailed plan before writing a single line of code.
• Test-Driven Development (TDD): Can be instructed to write failing tests first, then generate the minimal code required to make them pass, significantly accelerating the TDD cycle.

Let's dive into the techniques that will help you harness this power effectively.

1. Foundational Setup: The Core of Your Workflow​

A robust setup is the bedrock of an efficient workflow. Investing time here pays dividends in every subsequent interaction with Claude Code.

• Project Memory with CLAUDE.md: At the heart of any project is a concise CLAUDE.md file in the root directory. This file acts as the project's short-term memory, containing key architectural principles, coding standards, and testing procedures. To keep this file lean and focused, use imports like @docs/testing.md to reference more detailed documentation. You can quickly add new rules by starting a message with # or edit the memory directly with the /memory command.
• Monorepo Awareness: Modern development often involves monorepos. To grant Claude access to multiple packages for cross-directory analysis and refactoring, use the --add-dir flag or define additionalDirectories in your .claude/settings.json file. This is crucial for tasks that span multiple parts of your codebase.
• Keyboard & Terminal Ergonomics: Speed is essential. Master key shortcuts to streamline your interactions. Use Esc Esc to quickly edit your previous message. Enable Shift+Enter for newlines by running /terminal-setup once. For Vim enthusiasts, the /vim command enables familiar Vim-style motions for a more comfortable editing experience.

2. Streamlining Your Day-to-Day Workflow​

With a solid foundation, you can introduce practices that reduce friction and boost your daily productivity.

Using the Right Mode​

The CLI offers several permission modes to suit different tasks and risk appetites:

• default: The safest starting point. It prompts you for confirmation before performing potentially risky actions, offering a good balance of safety and speed.
• acceptEdits: A "live coding" mode that automatically accepts file edits without a prompt. It's ideal for rapid iteration and when you're closely supervising the process.
• plan: A "safe" mode designed for tasks like code reviews. Claude can analyze and discuss the code but cannot modify any files.
• bypassPermissions: Skips all permission prompts entirely. Use this mode with extreme caution and only in sandboxed environments where accidental changes have no consequence.

You can set a default mode in .claude/settings.json or specify one for a session with the --permission-mode flag.

Slash Commands & Customization​

Repetitive tasks are perfect candidates for automation. Turn your most common prompts into reusable tools by creating custom slash commands. Simply store them as Markdown files with YAML frontmatter in the .claude/commands/ directory.

• Use allowed-tools in the frontmatter to restrict what a command can do, adding a layer of safety.
• The ! prefix lets you run shell commands (e.g., !git status -sb) and inject their output directly into your prompt's context.
• Use $ARGUMENTS to pass parameters to your commands, making them flexible and more powerful.

Resuming and Parallelizing Work​

• claude --continue: Instantly jumps you back into your most recent session.
• claude --resume: Presents a list of past sessions, letting you pick up exactly where you left off.
• Git worktrees: For large-scale refactors, use git worktree to create isolated branches. This allows you to run separate Claude sessions in parallel, each with its own context, preventing confusion and collisions.

Output Styles for Collaboration​

• /output-style explanatory: Enriches responses with an "Insights" section, making it perfect for mentoring junior developers or explaining complex changes in a pull request.
• /output-style learning: Structures responses with TODO(human) placeholders, actively inviting you to collaborate and fill in the gaps.

3. Incorporating Quality & Safety​

True autonomy requires guardrails. Integrate quality checks and safety nets directly into your workflow to build with confidence.

Hooks for Guardrails​

Hooks are shell commands that automatically run at specific lifecycle events, offering a deterministic way to enforce standards. Configure them in .claude/settings.json.

• PreToolUse: Run checks before a tool is used. For example, you can block edits to sensitive files or require a corresponding test file to exist before allowing a write operation.
• PostToolUse: Automate cleanup tasks after a tool is used. This is perfect for running formatters like prettier or gofmt, as well as linters and quick tests after every edit.
• Notification: Send a desktop alert when Claude requires your input, so you can switch tasks without losing your place.

For example, let Mac notify you once the job is done - code ~/.claude/settings.json

{
 "hooks": {
      "Stop": [
        {
          "hooks": [
            {
              "type": "command",
              "command": "say \"job's done!\""
            }
          ]
        }
      ]
    }
}

Permissions and Security​

Define explicit allow, ask, and deny rules in your settings to manage tool access without constant prompting.

• Allow: Safe, routine operations like Bash(npm run test:*).
• Ask: Potentially risky actions you want to approve manually, such as Bash(git push:*).
• Deny: Critical security rules to prevent catastrophes, such as Read(./.env) or Read(./secrets/**).

Specialist Subagents​

For complex projects, you can define project-scoped agents with specific roles, like a code-reviewer, test-runner, or debugger. Each agent is configured with a limited toolset, preventing it from overstepping its purpose. Claude can either delegate tasks to the appropriate agent automatically or you can invoke one explicitly. See this repository for examples.

4. Advanced Workflows & Integrations​

Elevate your workflow by integrating visual context and external services, moving beyond basic file access.

Visual Context with Screenshots and Images​

A picture is worth a thousand words, especially when debugging UI issues. There are three reliable ways to provide images to Claude Code:

1. Paste from Clipboard: Take a screenshot to your clipboard and paste it directly into the terminal with Ctrl+V (note: on macOS, this is Ctrl+V, not Cmd+V).
2. Drag & Drop: Drag an image file (PNG, JPEG, GIF, WebP) from your file explorer directly into the CLI window.
3. Reference File Path: Simply include the local file path in your prompt, e.g., Analyze this screenshot: /path/to/screenshot.png.

Model Context Protocol (MCP) Integrations​

MCP enables Claude to connect to external services like Jira, GitHub, Notion, or Sentry. After adding and authenticating an MCP server, you can reference external resources in your prompts, such as Implement the feature described in JIRA-ENG-4521.

Non-Interactive & CI/CD Use​

For automation and scripting, use print mode with the -p flag.

• Combine it with --output-format json or --output-format stream-json to produce machine-readable output that can be piped to other tools like jq for further processing.
• Use --max-turns to set a hard limit on interactions, preventing runaway loops in your automated scripts.

5. Cost & Performance Hygiene​

Powerful models require mindful usage. Adopt these habits to manage your spend and optimize performance.

• Watch Spend: Use the /cost command at any time to get a real-time summary of your current session's cost.
• Intentional Model Selection: Use the most powerful model, like Opus, for high-level planning, complex reasoning, and initial strategy. Then, switch to a faster, more cost-effective model like Sonnet or Haiku for implementation, testing, and other routine tasks.
• Status Line: A popular community tip is to add a custom status line to your terminal that displays live cost and other useful information, such as the current Git branch. The ccusage tool is a common choice for this.

6. Starter Pack: A Ready-to-Use Configuration​

Here are several copy-pasteable configuration files to get you started quickly.

.claude/settings.json (Project-Shared)​

This file establishes project-wide permissions, hooks, and monorepo settings.

{
  "defaultMode": "acceptEdits",
  "permissions": {
    "allow": [
      "Read(**/*)",
      "Edit(src/**)",
      "Bash(npm run test:*)",
      "Bash(npm run lint:*)",
      "Bash(go test:*)",
      "Bash(git status:*)",
      "Bash(git diff:*)"
    ],
    "ask": [
      "Bash(git push:*)",
      "Bash(pnpm publish:*)",
      "Bash(npm publish:*)"
    ],
    "deny": [
      "Read(./.env)",
      "Read(./.env.*)",
      "Read(./secrets/**)"
    ],
    "additionalDirectories": ["../apps", "../packages", "../services"]
  },
  "hooks": {
    "PreToolUse": [
      {
        "matcher": "Edit|MultiEdit|Write",
        "hooks": [
          {
            "type": "command",
            "command": "python3 - <<'PY'\nimport json,sys\np=json.load(sys.stdin).get('tool_input',{}).get('file_path','')\nblock=['.env','/secrets/','.git/']\nsys.exit(2 if any(b in p for b in block) else 0)\nPY"
          }
        ]
      }
    ],
    "PostToolUse": [
      {
        "matcher": "Edit|MultiEdit|Write",
        "hooks": [
          { "type": "command", "command": "npx prettier --write . --loglevel silent || true" },
          { "type": "command", "command": "npm run -s lint || true" },
          { "type": "command", "command": "npm run -s test || true" }
        ]
      }
    ],
    "Notification": [
      { "matcher": "", "hooks": [ { "type": "command", "command": "command -v terminal-notifier >/dev/null && terminal-notifier -message 'Claude needs input' -title 'Claude Code' || true" } ] }
    ]
  },
  "statusLine": { "type": "command", "command": "~/.claude/statusline.sh" }
}

.claude/commands/commit.md​

This custom command uses shell output to draft a Conventional Commit message.

---
allowed-tools: Bash(git add:*), Bash(git status:*), Bash(git commit:*)
description: Create a conventional commit from current changes
---

## Context
- Status: !`git status -sb`
- Diff:   !`git diff --staged; git diff`

## Task
Write a Conventional Commit subject (<= 72 chars) and a concise body.
Call out BREAKING CHANGE if needed. Stage relevant files and commit.

.claude/agents/code-reviewer.md​

An agent definition for a specialist code reviewer.

---
name: code-reviewer
description: Senior review with focus on correctness, security, tests, readability, performance.
tools: Read, Grep, Glob, Bash
---

Return a checklist grouped by **Critical**, **Warnings**, and **Suggestions**.
Propose minimal patches where possible. Include test guidance for each critical item.

CLAUDE.md (Memory)​

A sample memory file defining working style, quality standards, and key project documents.

# Working style
- Start in **Plan mode**; outline approach, tests, and risks. Wait for approval.
- Execute in **small, reversible steps**; propose staged commits with diffs.
- Place generated docs in `docs/ai/`. Avoid ad-hoc files elsewhere.

# Code quality
- Prefer pure functions and dependency injection.
- JS/TS: strict TS, eslint + prettier; tests via vitest/jest.
- Go: table-driven tests; `gofmt`/`golangci-lint`.
- Security: never read `.env*` or `./secrets/**`; do not write tokens to disk.

# Project map
@README.md
@docs/architecture.md
@docs/testing.md

7. Troubleshooting and Final Thoughts​

• Image Paste Issues: If pasting from the clipboard doesn't work (a common issue in some Linux terminals), fall back to the reliable drag-and-drop or file path methods.
• Over-Eager Edits: Avoid the bypassPermissions(started by claude --dangerously-skip-permissions) mode in your daily workflow. A better approach is to use acceptEdits combined with well-defined allow/ask/deny rules. Always review diffs before merging.
• Memory Bloat: If you notice Claude starting to miss instructions, your CLAUDE.md may have grown too large. Shorten it by moving details into imported doc files. You can also restate key rules during a session to bring them back into focus, or use the /compact command to clean up session history.

Claude Code is more than just a code generator; it's a platform for building a highly effective, AI-augmented development process. By moving beyond basic prompts and adopting these intermediate and advanced techniques, you can establish a workflow that is faster, safer, and more collaborative. Experiment with these features, tailor them to your projects, and discover a new paradigm of software development.

While many companies are still exploring the potential of generative AI, some trailblazers have already woven it into their core operations, achieving impressive results. OpenAI's latest report, "AI in the Enterprise," distills seven universal principles for successful AI adoption in businesses, drawing from in-depth research into industry leaders like Morgan Stanley, Indeed, and Klarna. This isn't just a technological achievement—it's a shift in mindset, collaboration, and business value.

Seven Insights: From Exploration to Scalable Implementation​

1. Start with Rigorous Evaluation (Evals): Prioritize "Control" Before "Growth"​

Adopting AI isn't an overnight process. Before rolling it out widely, establishing a thorough, measurable evaluation system is crucial for success.

Take financial giant Morgan Stanley as an example. With sensitive client operations at stake, they didn't just follow trends blindly. Instead, they developed a multi-dimensional evaluation system focusing on three core areas—accuracy in language translation, quality of information summarization, and comparison with human expert answers. Only when the model was deemed "controllable, safe, and beneficial" did they gradually introduce it to frontline operations.

This cautious approach has paid off: now, 98% of Morgan Stanley's financial advisors use AI daily; the document hit rate in their internal knowledge base has soared from 20% to 80%; and client follow-ups that once took days are now completed in hours.

2. Deeply Embed AI into Product Experience, Rather Than Adding a Chatbot​

The most successful AI applications are those that seamlessly integrate into existing products, enhancing the core user experience. It should feel as natural as water or electricity in daily life.

Indeed, the world's largest job site, exemplifies this approach. Instead of merely adding a job search chatbot, they used GPT-4o mini to automatically generate personalized "recommendation reasons" for each system-matched job. This seemingly small tweak directly addresses job seekers' "why me" questions, significantly improving matching efficiency and user experience. As a result, job seekers' application initiation increased by 20%, and the employer successful hiring rate rose by 13%.

3. Act Early to Enjoy the "Compounding Snowball" of Knowledge and Experience​

AI's value grows through continuous iteration and learning. The earlier you start, the more your organization can benefit from this "compounding" effect.

Swedish fintech company Klarna's AI customer service system is a vivid example of this principle. In just a few months, AI customer service has handled two-thirds of customer chat sessions, effectively taking on the workload of hundreds of human agents. More impressively, the average resolution time for customer issues dropped from 11 minutes to 2 minutes. This initiative is expected to generate $40 million in annual profit growth for the company. Today, 90% of Klarna employees use AI in their daily work, enabling faster innovation and continuous optimization across the organization.

Every year, Stripe Sessions offers a window into the future of the internet economy. This year's event didn't disappoint, with the Collison brothers unveiling a vision of commerce that feels both imminent and transformative. Having digested the keynote, I'm struck by how clearly certain patterns are emerging in the evolving landscape of digital business.

Here are five crucial insights that stood out to me.

1. The Stripe Economy Has Become a Force of Nature​

The scale of Stripe's ecosystem has reached truly macroeconomic proportions:

• Businesses on Stripe grew 7x faster than the S&P 500 in 2024
• Their collective growth represented $400 billion in new payment volume
• Stripe now processes over $1.4 trillion annually — roughly 1.3% of global GDP
• Approximately 2 million US businesses (6% of all American companies) are building on Stripe

What's remarkable isn't just the scale but the breadth of adoption. From Fortune 100 giants to two-person startups, from AI labs to creator economy platforms, Stripe has effectively become the financial infrastructure layer for the internet.

When a single platform touches this much of the economy, its directional shifts matter. The internet economy is no longer a niche — it's increasingly the economy.

2. AI Companies Are Breaking All Growth Records​

The most striking revelation from the keynote was just how fast AI-native companies are scaling compared to previous generations of startups:

• New AI companies reach $5M ARR in just 9 months on average
• Lovable hit $50M ARR in 4 months
• Cursor has achieved over **$300M ARR in two years** with remarkable efficiency ($5M revenue per employee)

For context, SaaS companies typically took 18-24 months to reach similar milestones during their boom period. The acceleration is unprecedented.

What explains this hypergrowth? AI companies benefit from three advantages:

1. Immediate global reach — serving 200+ countries from day one is now standard
2. Higher retention rates than traditional SaaS
3. Lower operational complexity enabling lean teams to support massive user bases

This suggests we're witnessing not just a technology shift but a fundamental change in business velocity. The constraints that previously limited growth are being systematically removed.

3. Stablecoins Are Quietly Revolutionizing Global Finance​

While AI generates most headlines, stablecoins might ultimately deliver similar economic impact. Patrick Collison's description of stablecoins as "room temperature superconductors for value" perfectly captures their transformative potential.

Consider these developments:

• Stablecoin supply is up 39% since last year
• Leading stablecoin issuers are becoming major holders of US Treasuries
• Companies from SpaceX to smaller startups are using stablecoins to eliminate friction in global operations

The real breakthrough is how stablecoins solve the persistent challenge of borderless financial services. Businesses can now launch simultaneously in dozens of countries without navigating the complex web of local banking relationships and currency conversion.

This significantly lowers the barrier to global expansion and creates opportunities for entirely new business models centered around borderless value transfer.

4. "Agent Commerce" Will Redefine How We Buy Everything​

Perhaps the most forward-looking concept introduced was "Model-initiated Commerce Protocol" (MCP) — enabling AI agents to directly make purchases on behalf of users.

The demo showed Cursor (an AI coding assistant) purchasing Vercel's bot protection entirely within the coding environment, without ever leaving the workflow.

This points to a profound shift in commerce:

• AI tools will become native sales channels
• Purchases will happen contextually within workflows
• The traditional website/app checkout experience may become secondary

For businesses, this means rethinking distribution strategy entirely. Every AI tool becomes a potential point-of-sale, with agents mediating purchasing decisions based on user intent rather than explicit shopping behavior.

The implications for marketing, pricing, and customer acquisition are enormous. We're moving from search-driven commerce to intent-driven commerce, with AI interpreting and acting on needs before they're fully articulated.

5. The New Formula for Breakout Success Has Changed​

Beyond specific technologies, John Collison identified distinct patterns among today's fastest-growing companies:

Going Global Immediately​

The most successful startups now target international markets from day one rather than following the traditional domestic-first approach.

Extreme Specialization​

The internet's vast reach makes highly specialized offerings not just viable but advantageous. Companies like Harvey (legal AI) and Naba (healthcare AI) demonstrate how domain-specific focus drives rapid adoption.

Usage-Based Pricing​

AI economics and inference costs are driving a shift away from flat subscriptions toward outcome-based and usage-based pricing models.

Extraordinary Per-Employee Leverage​

Today's breakout companies achieve efficiency ratios that would have seemed impossible a decade ago. Gloss Genius supports 90,000 salons with just 300 employees.

These patterns represent a fundamental rethinking of business building. The traditional playbook for scaling a technology company is being rapidly rewritten.

What This Means for Founders and Investors​

For those building or investing in technology companies, several imperatives emerge:

1. Think globally from day one — geographical constraints are increasingly artificial

2. Embrace specificity — being the best solution for a narrow use case beats being adequate for many

3. Build for agent commerce — consider how your product will interface with AI assistants, not just human users

4. Integrate stablecoins early — reduce friction for global customers before competitors do

5. Optimize for retention — in the AI economy, sticky products with strong retention metrics are winning

The most exciting aspect of all this is that we're still early. Both AI and stablecoins are just beginning to reshape commerce. The companies being built today with these technologies as foundational elements will likely define the next decade of the internet economy.

As Patrick Collison noted, periods of technological turbulence historically favor bold innovation. For founders willing to embrace these shifts, the opportunity has never been greater.

What are your thoughts on the future of commerce? Are you seeing these patterns in your industry? Let me know in the comments.

Key Challenges with Monolithic LMs​

• Hard to control, debug, and improve.
• Every AI system makes mistakes.
• Modular systems (Compound AI) address these challenges.

Compound AI Systems​

• Modular programs use LMs as specialized components.
• Examples:
  • Retrieval-Augmented Generation.
  • Multi-Hop Retrieval-Augmented Generation.
  • Compositional Report Generation.
• Benefits:
  • Quality: Reliable LM composition.
  • Control: Iterative improvement via tools.
  • Transparency: Debugging and user-facing attribution.
  • Efficiency: Use smaller LMs and offload control flow.
  • Inference-time Scaling: Search for better outputs.

Anatomy of LM Programs in DSPy​

• Modules:

  • Define strategies for tasks.
  • Example: MultiHop uses Chain of Thought and retrieval.

• Program Components:

  • Signature: Task definition.
  • Adapter: Maps input/output to prompts.
  • Predictor: Applies inference strategies.
  • Metrics: Define objectives and constraints.
  • Optimizer: Refines instructions for desired behavior.

DSPy Optimization Methods​

1. Bootstrap Few-shot:

   • Generate examples using rejection sampling.

2. Extending OPRO:

   • Optimize instructions through prompting.

3. MIPRO:

   • Jointly optimize instructions and few-shot examples using Bayesian learning.

Key Benefits of DSPy​

• Simplifies programming for LMs.
• Optimized prompts for accuracy and efficiency.
• Enables modularity and scalability in AI systems.

Lessons and Research Directions​

1. Natural Language Programming:
   • Programs are more accurate, controllable, and transparent.
   • High-level optimizers bootstrap prompts and instructions.
2. Natural Language Optimization:
   • Effective grounding and credit assignment are crucial.
   • Optimizing both instructions and demonstrations enhances performance.
3. Future Directions:
   • Focus on modularity, better inference strategies, and optimized LM usage.

Summary​

• Compound AI Systems make LMs modular and reliable.
• DSPy provides tools to build, optimize, and deploy modular AI systems.
• Emphasizes modularity and systematic optimization for AI progress.

Reasoning is pivotal for advancing LLM capabilities

Introduction​

• Expectations for AI: Solving complex math problems, discovering scientific theories, achieving AGI.
• Baseline Expectation: AI should emulate human-like learning with few examples.

Key Concepts​

• What is Missing in ML?
  • Reasoning: The ability to logically derive answers from minimal examples.

Toy Problem: Last Letter Concatenation​

• Problem

  : Extract the last letters of words and concatenate them.

  • Example: "Elon Musk" → "nk".

• Traditional ML: Requires significant labeled data.

• LLMs: Achieve 100% accuracy with one demonstration using reasoning.

Importance of Intermediate Steps​

• Humans solve problems through reasoning and intermediate steps.
• Example:
  • Input: "Elon Musk"
  • Reasoning: Last letter of "Elon" = "n", of "Musk" = "k".
  • Output: "nk".

Advancements in Reasoning Approaches​

1. Chain-of-Thought (CoT) Prompting
   • Breaking problems into logical steps.
   • Examples from math word problems demonstrate enhanced problem-solving accuracy.
2. Least-to-Most Prompting
   • Decomposing problems into easier sub-questions for gradual generalization.
3. Analogical Reasoning
   • Adapting solutions from related problems.
   • Example: Finding the area of a square by recalling distance formula logic.
4. Zero-Shot and Few-Shot CoT
   • Triggering reasoning without explicit examples.
5. Self-Consistency in Decoding
   • Sampling multiple responses to improve step-by-step reasoning accuracy.

Limitations​

• Distraction by Irrelevant Context
  • Adding irrelevant details significantly lowers performance.
  • Solution: Explicitly instructing the model to ignore distractions.
• Challenges in Self-Correction
  • LLMs can fail to self-correct errors, sometimes worsening correct answers.
  • Oracle feedback is essential for effective corrections.
• Premise Order Matters
  • Performance drops with re-ordered problem premises, emphasizing logical progression.

Practical Implications​

• Intermediate reasoning steps are crucial for solving serial problems.
• Techniques like self-debugging with unit tests are promising for future improvements.

Future Directions​

1. Defining the right problem is critical for progress.
2. Solving reasoning limitations by developing models that autonomously address these issues.

Anthropic’s History​

• Founded: 2021 as a Public Benefit Corporation (PBC).
• Milestones:
  • 2022: Claude 1 completed.
  • 2023: Claude 1 released, Claude 2 launched.
  • 2024: Claude 3 launched.
  • 2025: Advances in interpretability and AI safety:
    • Mathematical framework for constitutional AI.
    • Sleeper agents and toy models of superposition.

Responsible Scaling Policy (RSP)​

• Definition: A framework to ensure safe scaling of AI capabilities.
• Goals:
  • Provide structure for safety decisions.
  • Ensure public accountability.
  • Iterate on safe decisions.
  • Serve as a template for policymakers.
• AI Safety Levels (ASL): Modeled after biosafety levels (BSL) for handling dangerous biological materials, aligning safety, security, and operational standards with a model’s catastrophic risk potential.
  • ASL-1: Smaller Models: No meaningful catastrophic risk (e.g., 2018 LLMs, chess-playing AIs).
  • ASL-2: Present Large Models: Early signs of dangerous capabilities (e.g., instructions for bioweapons with limited reliability).
  • ASL-3: Higher Risk Models: Models with significant catastrophic misuse potential or low-level autonomy.
  • ASL-4 and higher: Speculative Models: Future systems involving qualitative escalations in catastrophic risk or autonomy.
• Implementation:
  • Safety challenges and methods.
  • Case study: computer use.

Measuring Capabilities​

• Challenges: Benchmarks become obsolete.
• Examples:
  • Task completion time relative to humans: Claude 3.5 completes tasks in seconds compared to human developers’ 30 minutes.
  • Benchmarks:
    • SWE-bench: Assesses real-world software engineering tasks.
    • Aider’s benchmarks: Code editing and refactoring.
• Results:
  • Claude 3.5 Sonnet outperforms OpenAI o1 across key benchmarks.
  • Faster and cheaper: $3/Mtok input vs. OpenAI o1 at $15/Mtok input.

Claude 3.5 Sonnet Highlights​

• Agentic Coding and Game Development: Designed for efficiency and accuracy in real-world scenarios.
• Computer Use Demos:
  • Coding: Demonstrated advanced code generation and integration.
  • Operations: Showcased operational tasks with safety considerations.

AI Safety Measures​

• Focus Areas:
  • Scaling governance.
  • Capability measurement.
  • Collaboration with academia.
• Practical Safety:
  • ASL standard implementation.
  • Deployment safeguards.
  • Lessons learned in year one.

Future Directions​

• Scaling and governance improvements.
• Enhanced benchmarks and academic partnerships.
• Addressing interpretability and sleeper agent risks.

Key Trends​

• Skyrocketing Capabilities: Rapid advancements in LLMs since 2018.
• Declining Access: Shift from open paper, code, and weights to API-only models, limiting experimentation and research.

Why Access Matters​

• Access drives innovation:
  • 1990s: Digital text enabled statistical NLP.
  • 2010s: GPUs and crowdsourcing fueled deep learning and large datasets.
• Levels of access define research opportunities:
  • API: Like a cognitive scientist, measure behavior (prompt-response systems).
  • Open-Weight: Like a neuroscientist, probe internal activations for interpretability and fine-tuning.
  • Open-Source: Like a computer scientist, control and question every part of the system.

Levels of Access for Foundation Models​

1. API Access

   • Acts as a universal function (e.g., summarize, verify, generate).
   • Enables problem-solving agents (e.g., cybersecurity tools, social simulations).
   • Challenges: Deprecation and limited reproducibility.

2. Open-Weight Access

   • Enables interpretability, distillation, fine-tuning, and reproducibility.
   • Prominent models: Llama, Mistral.
   • Challenges:
     • Testing model independence and functional changes from weight modifications.
     • Blueprint constraints of pre-existing models.

3. Open-Source Access

   • Embodies creativity, transparency, and collaboration.
   • Examples: GPT-J, GPT-NeoX, StarCoder.
   • Performance gap persists compared to closed models due to compute and data limitations.

Key Challenges and Opportunities​

• Open-Source Barriers:
  • Legal restrictions on releasing web-derived training data.
  • Significant compute requirements for retraining.
• Scaling Compute:
  • Pooling idle GPUs.
  • Crowdsourced efforts like Big Science.
• Emergent Research Questions:
  • How do architecture and data shape behavior?
  • Can scaling laws predict performance at larger scales?

Reflections​

• Most research occurs within API and fixed-weight confines, limiting exploration.
• Open-weight models offer immense value for interpretability and experimentation.
• Open-source efforts require collective funding and infrastructure support.

Final Takeaway​

Access shapes the trajectory of innovation in foundation models. To unlock their full potential, researchers must question data, architectures, and algorithms while exploring new models of collaboration and resource pooling.

Key Challenges with LLMs​

• Difficulty with tasks requiring complex planning (e.g., travel itineraries, meeting schedules).
• Performance declines with increasing task complexity (e.g., more cities, people, or constraints).

Three Proposed Solutions​

1. Scaling Law
   • Increase data, compute, and model size.
   • Limitation: High costs and diminishing returns for reasoning/planning tasks.
2. Hybrid Systems
   • Combine deep learning models with symbolic solvers. Symbolic reasoning refers to the process of solving problems and making decisions using explicit symbols, rules, and logic. It is a method where reasoning is based on clearly defined relationships and representations, often following formal logic or mathematical principles.
   • Approaches:
     • End-to-End Integration: Unified deep model and symbolic system.
     • Data Augmentation: Neural models provide structured data for solvers.
     • Tool Use: LLMs act as interfaces for external solvers.
   • Notable Examples:
     • MILP Solvers: For travel planning with constraints.
     • Searchformer: Transformers trained to emulate A* search.
     • DualFormer: Switches dynamically between fast (heuristic) and slow (deliberative) reasoning.
     • SurCo: Combines combinatorial optimization with latent space representations.
3. Emerging Symbolic Structures
   • Exploration of symbolic reasoning emerging in neural networks.
   • Findings:
     • Neural networks exhibit Fourier-like patterns in arithmetic tasks.
     • Gradient descent produces solutions aligned with algebraic constructs.
     • Emergent ring homomorphisms and symbolic efficiency in complex tasks.

Research Implications​

• Neural networks naturally learn symbolic abstractions, offering potential for improved reasoning.
• Hybrid systems might represent the optimal balance between adaptability (neural) and precision (symbolic).
• Advanced algebraic techniques could eventually replace gradient descent.

Overall Takeaway​

The future of decision-making AI lies in leveraging both neural adaptability and symbolic rigor. Hybrid approaches appear most promising for solving tasks requiring both perception and structured reasoning.

Key Themes and Context​

Enterprise Workflows

• Automation levels range from scripted workflows (minimal variation) to agentic workflows (adaptive and dynamic).
• Enterprise environments, such as those supported by ServiceNow, involve complex, repetitive tasks like IT management, CRM updates, and scheduling.
• The adoption of LLM-powered agents (e.g., API agents and Web agents) transforms these workflows by leveraging capabilities like multimodal observations and dynamic actions.

LLM Agents for Enterprise Workflows

• API Agents
  • Utilize structured API calls for efficiency.
  • Pros: Low latency, structured inputs.
  • Cons: Depend on predefined APIs, limited adaptability.
• Web Agents
  • Simulate human actions on web interfaces.
  • Pros: Greater flexibility; can interact with dynamic UIs.
  • Cons: High latency, error-prone.

WorkArena Framework

• Benchmarks designed for realistic enterprise workflows.
• Tasks range from IT inventory management to budget allocation and employee offboarding.
• Supported by BrowserGym and AgentLab for testing and evaluation in simulated environments.

Technical Frameworks​

Agent Architectures

• TapeAgents Framework

  • Represents agents as resumable modular state machines.
  • Features structured logs (the "tape") for actions, thoughts, and outcomes.
  • Facilitates optimization (e.g., fine-tuning from teacher-to-student agents).

• WorkArena++

  • Extends WorkArena with more compositional and challenging tasks.
  • Evaluates agents on capabilities like long-term planning and multimodal data integration.

Benchmarks

• WorkArena: ~20k unique enterprise task instances.
• WorkArena++: Focused on compositional workflows and data-driven reasoning.
• Other tools: MiniWoB, WebLINX, VisualWebArena.

Evaluation Metrics

• GREADTH (Grounded, Responsive, Accurate, Disciplined, Transparent, Helpful):
  • Prioritizes real-world agent performance metrics.
• Task-Specific Success Rates:
  • For example, form-filling assistants evaluated at 300x lower cost than GPT-4 through fine-tuned students.

Challenges for Agents in Workflows​

• Context Understanding
  • Enterprise tasks require understanding deep hierarchies of information (e.g., dashboards, KBs).
  • Sparse rewards in benchmarks complicate learning.
• Long-Term Planning
  • Subgoal decomposition and multi-step task execution remain difficult.
• Safety and Alignment
  • Risks from malicious inputs (e.g., adversarial prompts, hidden text).
• Cost and Efficiency
  • Shrinking context windows and modular architectures are key to reducing compute costs.

Future Directions​

Augmentation Models

• Centaur Framework:
  • Separates AI from human tasks (e.g., content gathering by AI, final editing by humans).
• Cyborg Framework:
  • Promotes tight collaboration between AI and humans.

Unified Evaluation

• Calls for a meta-benchmark to consolidate evaluation protocols across platforms (e.g., WebLINX, WorkArena).

Advancements in Agent Optimization

• Leveraging RL-inspired techniques for fine-tuning.
• Modular learning frameworks to improve generalizability.

Opportunities in Knowledge Work​

• Automation of repetitive, low-value tasks (e.g., scheduling, report generation).
• Integration of multimodal agents into enterprise environments to support decision-making and strategic tasks.
• Enhanced productivity through human-AI collaboration models.

This synthesis connects the theoretical and practical elements of enterprise workflow agents, showcasing their transformative potential while addressing current limitations.

Introduction​

• Two kinds of AI applications:

  • Generative AI: Creates content like text and images.
  • Agentic AI: Performs complex tasks autonomously. This is the future.

• Key Question: How can developers make these systems easier to build?

Agentic AI Frameworks​

• Examples:

  • Applications include personal assistants, autonomous robots, gaming agents, web/software agents, science, healthcare, and supply chains.

• Core Benefits:

  • User-Friendly: Natural and intuitive interactions with minimal input.
  • High Capability: Handles complex tasks efficiently.
  • Programmability: Modular and maintainable, encouraging experimentation.

• Design Principles:

  • Unified abstractions integrating models, tools, and human interaction.
  • Support for dynamic workflows, collaboration, and automation.

AutoGen Framework​

https://github.com/microsoft/autogen

• Purpose: A framework for building agentic AI applications.

• Key Features:

  • Conversable and Customizable Agents: Simplifies building applications with natural language interactions.
  • Nested Chat: Handles complex workflows like content creation and reasoning-intensive tasks.
  • Group Chat: Supports collaborative task-solving with multiple agents.

• History:

  • Started in FLAML (2022), became standalone (2023), with over 200K monthly downloads and widespread adoption.

Applications and Examples​

• Advanced Reflection:
  • Two-agent systems for collaborative refinement of tasks like blog writing.
• Gaming and Strategy:
  • Conversational Chess, where agents simulate strategic reasoning.
• Enterprise and Research:
  • Applications in supply chains, healthcare, and scientific discovery, such as ChemCrow for discovering novel compounds.

Core Components of AutoGen​

• Agentic Programming:
  • Divides tasks into manageable steps for easier scaling and validation.
• Multi-Agent Orchestration:
  • Supports dynamic workflows with centralized or decentralized setups.
• Agentic Design Patterns:
  • Covers reasoning, planning, tool integration, and memory management.

Challenges in Agent Design​

• System Design:
  • Optimizing multi-agent systems for reasoning, planning, and diverse applications.
• Performance:
  • Balancing quality, cost, and scalability while maintaining resilience.
• Human-AI Collaboration:
  • Designing systems for safe, effective human interaction.

Open Questions and Future Directions​

• Multi-Agent Topologies:
  • Efficiently balancing centralized and decentralized systems.
• Teaching and Optimization:
  • Enabling agents to learn autonomously using tools like AgentOptimizer.
• Expanding Applications:
  • Exploring new domains such as software engineering and cross-modal systems.

Trajectory and potential of LLM agents

Introduction​

• Definition of Agents: Intelligent systems interacting with environments (physical, digital, or human).
• Evolution: From symbolic AI agents like ELIZA(1966) to modern LLM-based reasoning agents.

Core Concepts​

1. Agent Types:
   • Text Agents: Rule-based systems like ELIZA(1966), limited in scope.
   • LLM Agents: Utilize large language models for versatile text-based interaction.
   • Reasoning Agents: Combine reasoning and acting, enabling decision-making across domains.
2. Agent Goals:
   • Perform tasks like question answering (QA), game-solving, or real-world automation.
   • Balance reasoning (internal actions) and acting (external feedback).

Key Developments in LLM Agents​

1. Reasoning Approaches:
   • Chain-of-Thought (CoT): Step-by-step reasoning to improve accuracy.
   • ReAct Paradigm: Integrates reasoning with actions for systematic exploration and feedback.
2. Technological Milestones:
   • Zero-shot and Few-shot Learning: Achieving generality with minimal examples.
   • Memory Integration: Combining short-term (context-based) and long-term memory for persistent learning.
3. Tools and Applications:
   • Code Augmentation: Enhancing computational reasoning through programmatic methods.
   • Retrieval-Augmented Generation (RAG): Leveraging external knowledge sources like APIs or search engines.
   • Complex Task Automation: Embodied reasoning in robotics and chemistry, exemplified by ChemCrow.

Limitations​

• Practical Challenges:
  • Difficulty in handling real-world environments (e.g., decision-making with incomplete data).
  • Vulnerability to irrelevant or adversarial context.
• Scalability Issues:
  • Real-world robotics vs. digital simulation trade-offs.
  • High costs of fine-tuning and data collection in specific domains.

Research Directions​

• Unified Solutions: Simplifying diverse tasks into generalizable frameworks (e.g., ReAct for exploration and decision-making).
• Advanced Memory Architectures: Moving from append-only logs to adaptive, writeable long-term memory systems.
• Collaboration with Humans: Focusing on augmenting human creativity and problem-solving capabilities.

Future Outlook​

• Emerging Benchmarks:
  • SWE-Bench for software engineering tasks.
  • FireAct for fine-tuning LLM agents in dynamic environments.
• Broader Impacts:
  • Enhanced digital automation.
  • Scalable solutions for complex problem-solving in domains like software engineering, scientific discovery, and web automation.

The story of TianPan.co mirrors the evolution of web publishing itself - from simple HTML pages to today's AI-augmented content platforms. As we launch version 3, I want to share how we're reimagining what a modern publishing platform can be in the age of AI.

The Journey: From WordPress to AI-Native​

Like many technical blogs, TianPan.co started humbly in 2009 as a WordPress site on a free VPS. The early days were simple: write, publish, repeat. But as technology evolved, so did our needs. Version 1 moved to Octopress and GitHub, embracing the developer-friendly approach of treating content as code. Version 2 brought modern web technologies with GraphQL, server-side rendering, and a React Native mobile app.

But the landscape has changed dramatically. AI isn't just a buzzword - it's transforming how we create, organize, and share knowledge. This realization led to Version 3, built around a radical idea: what if we designed a publishing system with AI at its core, not just as an add-on?

The Architecture of an AI-Native Platform​

Version 3 breaks from traditional blogging platforms in several fundamental ways:

1. Content as Data: Every piece of content is stored as markdown, making it instantly processable by AI systems. This isn't just about machine readability - it's about enabling AI to become an active participant in the content lifecycle.

2. Distributed Publishing, Centralized Management: Content flows automatically from our central repository to multiple channels - Telegram, Discord, Twitter, and more. But unlike traditional multi-channel publishing, AI helps maintain consistency and optimize for each platform.

3. Infrastructure Evolution: We moved from a basic 1 CPU/1GB RAM setup to a more robust infrastructure, not just for reliability but to support AI-powered features like real-time content analysis and automated editing.

The technical architecture reflects this AI-first approach:

.
├── _inbox            # AI-monitored draft space
├── notes             # published English notes
├── notes-zh          # published Chinese notes
├── crm               # personal CRM
├── ledger            # my beancount.io ledger
├── packages
│   ├── chat-tianpan  # LlamaIndex-powered content interface
│   ├── website       # tianpan.co source code
│   ├── prompts       # AI system prompts
│   └── scripts       # AI processing pipeline

Beyond Publishing: An Integrated Knowledge System​

What makes Version 3 unique is how it integrates multiple knowledge streams:

• Personal CRM: Relationship management through AI-enhanced note-taking
• Financial Tracking: Integrated ledger system via beancount.io
• Multilingual Support: Automated translation and localization
• Interactive Learning: AI-powered chat interface for deep diving into content

The workflow is equally transformative:

1. Content creation starts in markdown
2. CI/CD pipelines trigger AI processing
3. Zapier integrations distribute across platforms
4. AI editors continuously suggest improvements through GitHub issues

Looking Forward: The Future of Technical Publishing​

This isn't just about building a better blog - it's about reimagining how we share technical knowledge in an AI-augmented world. The system is designed to evolve, with each component serving as a playground for experimenting with new AI capabilities.

What excites me most isn't just the technical architecture, but the possibilities it opens up. Could AI help surface connections between seemingly unrelated technical concepts? Could it help make complex technical content more accessible to broader audiences? Will it be possible to easily produce multimedia content in the future?

These are the questions we're exploring with TianPan.co v3. It's an experiment in using AI not just as a tool, but as a collaborative partner in creating and sharing knowledge.