See files

Fully Homomorphic Encryption (FHE) is a powerful cryptographic method that allows computers to perform calculations directly on encrypted data without ever decrypting it. In simple terms, it enables a user to send locked data to a server, have the server process that data while it remains locked, and then receive a locked result that only the user can unlock to reveal the correct answer.[1][2][6][9]

The process generally works through the mathematical steps outlined below.

The term "homomorphic" refers to the mathematical structure of the encryption. In FHE, the algebraic operations performed on the ciphertext (encrypted data) translate directly to the corresponding operations on the plaintext (original data).[2][5]

This capability is usually achieved through two fundamental operations:

• Addition: If you add two encrypted values together, the result—when decrypted—equals the sum of the two original values.
• Multiplication: If you multiply two encrypted values,

Here are 10 fringe software technologies that could become mainstream in the coming years:

Brain-inspired computing systems that mimic neural architectures for extreme energy efficiency. 2025 is considered the commercial breakthrough year, with chips like BrainChip Akida and Intel Loihi 2 enabling AI at ultra-low power. Technical targets aim for 100× efficiency improvements by 2030.[1][2]

Encryption that allows computations on encrypted data without decryption. Apple already uses it in iOS for privacy-preserving caller ID lookups. Some experts predict mainstream adoption in 1-2 years, while others estimate 5-10 years due to performance challenges.[3][4]

WASM is evolving from browser tech to a universal binary format for servers, edge computing, and IoT devices. Projects like WASI aim to let code "run anywhere"—desktop, cloud, embedded systems—securely and portably.[5][6]

Perfect! I found the blog post you were looking for. It's "Climbing the Wrong Hill" by Chris Dixon (not from mathewanders.com), published in September 2009. This is the exact post that uses the mountain peak and local maxima metaphor you described.

The Core Metaphor:

Chris Dixon uses a computer science concept called "hill climbing" to explain why smart, ambitious people often get stuck pursuing the wrong career path. Here's how he explains it:[1]

Based on the Fizzy project and broader software development practices, the project appears to use hash-based versioning (or a variant combining git commit hashes with dates), which represents a fundamentally different philosophy from semantic versioning.[^1_1][^1_2]

Hash-based versioning uses git commit hashes—typically shortened to 7-8 characters—as version identifiers. These are alphanumeric strings like 9ce4987 or 3f9a7c1d that uniquely identify a specific commit in the repository. This approach is increasingly common in continuous deployment environments where releases happen frequently, sometimes multiple times per day.[^1_3][^1_4][^1_5][^1_6][^1_7][^1_8]

Here are 12 unicorn level product positions that no one is working on, but should

1. Decentralized github (p2p, annonymity, distributed data store)
2. Git history 2 usefull timeline story telling (investor updates, SoMe, slack recaps)
3. Decentralized slack. slack is a just a nice mirc wrapper. how hard could it be?
4. Decentralized analytics platform. (google analytics but distributed, no lockin, no schenangance)
5. Cursor that can run 10 agents at the same time with different models, in different branches. then converge when done
6. IDE focused on local LLM. GPT OSS 120B is as good as GPT5 with agentic coding etc. But you need an m5 at 10k$ to run it

Looking through the MCP servers repository you provided, I found several servers for analytics platforms, but they are platform-specific rather than multi-platform readers. Here are the analytics-focused MCP servers available:

Official Integrations:

• Axiom - Query and analyze logs, traces, and event data
• Comet Opik - Query logs, traces, prompts and LLM telemetry data
• Dynatrace - Real-time observability and monitoring platform
• Grafana - Search dashboards, investigate incidents, query datasources
• Honeycomb - Query data, alerts, dashboards (Enterprise customers)

/Users/eon/conduit.design/_docs/architecture_and_structure_guide.md /Users/eon/conduit.design/_docs/code_style_guide.md please read these documents. then scan the code base for lowkey low hangingfruit low risk items that doesnt follow these guids. then make a list of at least 20-40 items and save it to a markdown file for later refactoring. do not refactor yet. just search analyse and make the list

Now let me create detailed monthly timelines for each month in the same format as the August example:

The Challenge: Build a robust Figma plugin MCP server from the ground up with enterprise-grade patterns.

What We Solved:

🏗️ Plugin-Server Communication - WebSocket-based bidirectional messaging

Problem: How do we let AI talk to Figma?

What We Built:

• Real-time communication channel between AI and Figma