I built a Serverless OpenAI Gateway to cut costs by 30% and sanitize PII (Open Source)

<p><a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fc4khcb83p8s23seu2u12.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fc4khcb83p8s23seu2u12.png" alt="Dashboard Preview" width="800" height="564"></a></p> <h2> canonical_url: <a href="https://github.com/guimaster97/pii-sanitizer-gateway" rel="noopener noreferrer">https://github.com/guimaster97/pii-sanitizer-gateway</a> </h2> <p>If you are building LLM wrappers or internal tools, you probably noticed two things killing your margins (and your sleep):</p> <ol> <li> <strong>Redundant API Costs:</strong> Users asking the same questions over and over, forcing you to pay OpenAI for the same tokens.</li> <li> <strong>Compliance Anxiety:</strong> The fear of a user accidentally pasting a client's Name, Email, or Tax ID into your chatbot, which then gets sent to a third-party server (OpenAI/DeepSeek).</li> </ol> <p>I looked for solutions, but most were heavy Enterprise Gateways (Java/Docker) or expensive SaaS. So, I decided to engineer my own solution running entirely on the Edge using <strong>Cloudflare Workers</strong>.</p> <p>Here is how I built <strong>Sanitiza.AI</strong>, an open-source gateway that caches requests and scrubs PII before they leave your network.</p> <h2> 🏗️ The Architecture </h2> <p>The goal was <strong>Zero DevOps</strong>. No Docker containers to manage, no Redis instances to pay for. Just pure Serverless functions.</p> <ul> <li> <strong>Runtime:</strong> Cloudflare Workers (TypeScript)</li> <li> <strong>Framework:</strong> Hono (A lightweight web framework, like Express but for the Edge)</li> <li> <strong>Storage:</strong> Cloudflare KV (Key-Value store for caching)</li> <li> <strong>Logic:</strong> Native Web Crypto API for hashing.</li> </ul> <h3> 1. The Money Saver: Smart Caching </h3> <p>For RAG (Retrieval-Augmented Generation) apps, redundancy is huge. To solve this, I implemented a "Smart Cache" mechanism.</p> <p>Before forwarding a request to OpenAI, the Worker creates a <strong>SHA-256 hash</strong> of the request body (the prompt + system instructions).</p> <div class="highlight js-code-highlight"> <pre class="highlight plaintext"><code> typescript // How we create a unique fingerprint for the request async function generateHash(message: string) { const msgBuffer = new TextEncoder().encode(message); const hashBuffer = await crypto.subtle.digest('SHA-256', msgBuffer); const hashArray = Array.from(new Uint8Array(hashBuffer)); return hashArray.map(b => b.toString(16).padStart(2, '0')).join(''); } It then checks Cloudflare KV. Hit: Returns the stored JSON instantly (<50ms latency). Cost: $0. Miss: Forwards to OpenAI, gets the response, and saves it to KV for 24 hours. The Result: In my internal tools, I achieved a ~30% cache hit rate, effectively cutting my OpenAI bill by a third. 2. The Shield: PII Sanitization Sending PII (Personally Identifiable Information) to external LLMs is a GDPR/LGPD nightmare. I implemented a hybrid sanitization engine that runs before the request leaves the Edge. It uses Regex patterns (and can be extended with NER models) to identify sensitive entities. Example Input: "Analyze the contract for john.doe@email.com regarding the debt of $50k." What OpenAI Receives: "Analyze the contract for [EMAIL_HIDDEN] regarding the debt of [MONEY_HIDDEN]." The Worker maintains a mapping of these placeholders. When the LLM responds, the Worker "re-hydrates" the response, putting the real data back in if necessary, so the user never knows it was hidden. 3. The ROI Dashboard Engineering is useless if you can't prove value. I built a simple Admin Dashboard (served by the Worker itself via Hono) that tracks every token saved. (You can see the live calculator in the repo) 🚀 Performance on the Edge The biggest advantage of using Cloudflare Workers over a Python Proxy (like LiteLLM) is latency. The code runs in data centers close to the user. Cold Start: 0ms (practically). Cache Response Time: < 50ms. Throughput: Cloudflare's network handles the load balancing. 💻 The Code (Open Source) I decided to open-source the core engine under the MIT license. You can deploy it to your own Cloudflare account in about 2 minutes using the "Deploy" button. It works with OpenAI, DeepSeek, Groq, and any other compatible API. Repository: github.com/guimaster97/pii-sanitizer-gateway I'm currently looking for contributors to help implement Semantic Caching (using Vectorize) to catch prompts that are similar but not identical. If you are into Rust/WASM or Vector Databases, let's talk! If you found this useful, drop a star on the repo. It helps a lot! </code></pre> </div>

Google's Gemini Gradient Design Draws Parallels with 1984 Macintosh

<h2> Google Compares Gemini's Gradient Design to 1984 Macintosh </h2> <p>Recently, Google made an interesting comparison between the gradient design of Gemini, one of its latest products, and the iconic design of the Macintosh released in 1984. This comparison sparked a debate about the evolution of design in the tech world and how classic design elements can still influence modern products.</p> <h2> Background of the 1984 Macintosh </h2> <p>The 1984 Macintosh, officially known as the Macintosh 128k, was a revolutionary personal computer introduced by Apple. Released on January 24, 1984, this Macintosh was one of the first computers to use a graphical user interface (GUI) and gained widespread attention due to its iconic television commercial directed by Ridley Scott, which referenced George Orwell's dystopian novel, "1984". The commercial featured an athlete throwing a hammer at a screen displaying Big Brother's face, symbolizing that Macintosh would free users from IBM's dominance in personal computing.</p> <h2> Gemini's Gradient Design </h2> <p>Gemini, on the other hand, is Google's latest product featuring an attractive gradient design. While gradient design is not new in the world of design, its implementation on Gemini caught attention due to its resemblance to the 80s design aesthetic, including the 1984 Macintosh. The gradient on Gemini gives a dynamic and modern feel, showing how design can evolve while still retaining iconic elements from the past.</p> <h2> Google's Comparison </h2> <p>By comparing Gemini's gradient design to the 1984 Macintosh, Google highlights how design in technology continues to evolve in a circle. Although technology has advanced significantly over the past few decades, the principles of good design remain the same: creating intuitive, aesthetically pleasing, and functional interfaces. This comparison also shows how Google values design heritage in the tech industry and strives to integrate classic design elements into modern products.</p> <h2> Impact on the Design Industry </h2> <p>Google's comparison of Gemini's design to the 1984 Macintosh can have a significant impact on the design industry. It shows that designers don't always need to create something entirely new but can draw inspiration from iconic past designs and adapt them to meet modern needs and technology. This approach can help create products that are not only innovative but also have a classic touch that can be appreciated by users across different generations.</p> <h2> Conclusion </h2> <p>Google's comparison of Gemini's gradient design to the 1984 Macintosh highlights the evolution of design in the tech industry and how classic design elements can continue to influence modern products. It demonstrates that good design is about striking a balance between innovation and heritage, as well as the ability to draw inspiration from the past while looking towards the future.</p>

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The $100 Billion Megadeal Between OpenAI and Nvidia Is on Ice

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I built a Serverless OpenAI Gateway to cut costs by 30% and sanitize PII (Open Source)

<p><a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fc4khcb83p8s23seu2u12.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fc4khcb83p8s23seu2u12.png" alt="Dashboard Preview" width="800" height="564"></a></p> <h2> canonical_url: <a href="https://github.com/guimaster97/pii-sanitizer-gateway" rel="noopener noreferrer">https://github.com/guimaster97/pii-sanitizer-gateway</a> </h2> <p>If you are building LLM wrappers or internal tools, you probably noticed two things killing your margins (and your sleep):</p> <ol> <li> <strong>Redundant API Costs:</strong> Users asking the same questions over and over, forcing you to pay OpenAI for the same tokens.</li> <li> <strong>Compliance Anxiety:</strong> The fear of a user accidentally pasting a client's Name, Email, or Tax ID into your chatbot, which then gets sent to a third-party server (OpenAI/DeepSeek).</li> </ol> <p>I looked for solutions, but most were heavy Enterprise Gateways (Java/Docker) or expensive SaaS. So, I decided to engineer my own solution running entirely on the Edge using <strong>Cloudflare Workers</strong>.</p> <p>Here is how I built <strong>Sanitiza.AI</strong>, an open-source gateway that caches requests and scrubs PII before they leave your network.</p> <h2> 🏗️ The Architecture </h2> <p>The goal was <strong>Zero DevOps</strong>. No Docker containers to manage, no Redis instances to pay for. Just pure Serverless functions.</p> <ul> <li> <strong>Runtime:</strong> Cloudflare Workers (TypeScript)</li> <li> <strong>Framework:</strong> Hono (A lightweight web framework, like Express but for the Edge)</li> <li> <strong>Storage:</strong> Cloudflare KV (Key-Value store for caching)</li> <li> <strong>Logic:</strong> Native Web Crypto API for hashing.</li> </ul> <h3> 1. The Money Saver: Smart Caching </h3> <p>For RAG (Retrieval-Augmented Generation) apps, redundancy is huge. To solve this, I implemented a "Smart Cache" mechanism.</p> <p>Before forwarding a request to OpenAI, the Worker creates a <strong>SHA-256 hash</strong> of the request body (the prompt + system instructions).</p> <div class="highlight js-code-highlight"> <pre class="highlight plaintext"><code> typescript // How we create a unique fingerprint for the request async function generateHash(message: string) { const msgBuffer = new TextEncoder().encode(message); const hashBuffer = await crypto.subtle.digest('SHA-256', msgBuffer); const hashArray = Array.from(new Uint8Array(hashBuffer)); return hashArray.map(b => b.toString(16).padStart(2, '0')).join(''); } It then checks Cloudflare KV. Hit: Returns the stored JSON instantly (<50ms latency). Cost: $0. Miss: Forwards to OpenAI, gets the response, and saves it to KV for 24 hours. The Result: In my internal tools, I achieved a ~30% cache hit rate, effectively cutting my OpenAI bill by a third. 2. The Shield: PII Sanitization Sending PII (Personally Identifiable Information) to external LLMs is a GDPR/LGPD nightmare. I implemented a hybrid sanitization engine that runs before the request leaves the Edge. It uses Regex patterns (and can be extended with NER models) to identify sensitive entities. Example Input: "Analyze the contract for john.doe@email.com regarding the debt of $50k." What OpenAI Receives: "Analyze the contract for [EMAIL_HIDDEN] regarding the debt of [MONEY_HIDDEN]." The Worker maintains a mapping of these placeholders. When the LLM responds, the Worker "re-hydrates" the response, putting the real data back in if necessary, so the user never knows it was hidden. 3. The ROI Dashboard Engineering is useless if you can't prove value. I built a simple Admin Dashboard (served by the Worker itself via Hono) that tracks every token saved. (You can see the live calculator in the repo) 🚀 Performance on the Edge The biggest advantage of using Cloudflare Workers over a Python Proxy (like LiteLLM) is latency. The code runs in data centers close to the user. Cold Start: 0ms (practically). Cache Response Time: < 50ms. Throughput: Cloudflare's network handles the load balancing. 💻 The Code (Open Source) I decided to open-source the core engine under the MIT license. You can deploy it to your own Cloudflare account in about 2 minutes using the "Deploy" button. It works with OpenAI, DeepSeek, Groq, and any other compatible API. Repository: github.com/guimaster97/pii-sanitizer-gateway I'm currently looking for contributors to help implement Semantic Caching (using Vectorize) to catch prompts that are similar but not identical. If you are into Rust/WASM or Vector Databases, let's talk! If you found this useful, drop a star on the repo. It helps a lot! </code></pre> </div>

Google's Gemini Gradient Design Draws Parallels with 1984 Macintosh

<h2> Google Compares Gemini's Gradient Design to 1984 Macintosh </h2> <p>Recently, Google made an interesting comparison between the gradient design of Gemini, one of its latest products, and the iconic design of the Macintosh released in 1984. This comparison sparked a debate about the evolution of design in the tech world and how classic design elements can still influence modern products.</p> <h2> Background of the 1984 Macintosh </h2> <p>The 1984 Macintosh, officially known as the Macintosh 128k, was a revolutionary personal computer introduced by Apple. Released on January 24, 1984, this Macintosh was one of the first computers to use a graphical user interface (GUI) and gained widespread attention due to its iconic television commercial directed by Ridley Scott, which referenced George Orwell's dystopian novel, "1984". The commercial featured an athlete throwing a hammer at a screen displaying Big Brother's face, symbolizing that Macintosh would free users from IBM's dominance in personal computing.</p> <h2> Gemini's Gradient Design </h2> <p>Gemini, on the other hand, is Google's latest product featuring an attractive gradient design. While gradient design is not new in the world of design, its implementation on Gemini caught attention due to its resemblance to the 80s design aesthetic, including the 1984 Macintosh. The gradient on Gemini gives a dynamic and modern feel, showing how design can evolve while still retaining iconic elements from the past.</p> <h2> Google's Comparison </h2> <p>By comparing Gemini's gradient design to the 1984 Macintosh, Google highlights how design in technology continues to evolve in a circle. Although technology has advanced significantly over the past few decades, the principles of good design remain the same: creating intuitive, aesthetically pleasing, and functional interfaces. This comparison also shows how Google values design heritage in the tech industry and strives to integrate classic design elements into modern products.</p> <h2> Impact on the Design Industry </h2> <p>Google's comparison of Gemini's design to the 1984 Macintosh can have a significant impact on the design industry. It shows that designers don't always need to create something entirely new but can draw inspiration from iconic past designs and adapt them to meet modern needs and technology. This approach can help create products that are not only innovative but also have a classic touch that can be appreciated by users across different generations.</p> <h2> Conclusion </h2> <p>Google's comparison of Gemini's gradient design to the 1984 Macintosh highlights the evolution of design in the tech industry and how classic design elements can continue to influence modern products. It demonstrates that good design is about striking a balance between innovation and heritage, as well as the ability to draw inspiration from the past while looking towards the future.</p>

The $100 Billion Megadeal Between OpenAI and Nvidia Is on Ice

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