add fn acquisition blog

blog/fluxninja-acquisition-2024-03-17/blog.md

@@ -0,0 +1,120 @@
+---
+slug: fluxninja-joins-coderabbit
+title: FluxNinja joins CodeRabbit
+description:
+  CodeRabbit has acquired FluxNinja, a startup that provides a platform for
+  building scalable generative AI applications.
+authors: [gur, vishu]
+tags:
+  [
+    "AI",
+    "Acquisition",
+    "Generative AI",
+    "Code Review",
+    "Load Management",
+    "Rate Limiting",
+    "Caching",
+    "Scheduler",
+  ]
+image: ./preview.png
+---
+
+![FluxNinja joins CodeRabbit](./preview.png)
+
+We are excited to announce that CodeRabbit has acquired
+[FluxNinja](https://fluxninja.com), a startup that provides a platform for
+building scalable generative AI applications. This acquisition will allow us to
+ship new use cases at an industrial-pace while sustaining our rapidly growing
+user base. FluxNinja's Aperture product provides advanced rate & concurrency
+limiting, caching, and request prioritization capabilities that are essential
+for reliable and cost-effective AI workflows.
+
+<!--truncate-->
+
+Since our launch,
+[Aperture's open-source](https://github.com/fluxninja/aperture) core engine has
+been critical to our infrastructure. Our initial use case centered around
+[mitigating aggressive rate limits](../openai-rate-limits-2023-10-23/blog.md)
+imposed by OpenAI, allowing us to prioritize paid and real-time chat users
+during peak load hours while queuing requests from the free users. Further, we
+used Aperture's
+[caching and rate-limiting capabilities](../how-we-built-cost-effective-generative-ai-application-2023-12-23/blog.md)
+to manage costs that in turn allowed us to offer open-source developers a fully
+featured free tier by minimizing abuse. These capabilities allowed us to scale
+our user base without ever putting up a waitlist and at a price point that is
+sustainable for us. With Aperture's help, CodeRabbit has scaled to over 100K
+repositories and several thousand organizations under its review in a short
+period.
+
+We started CodeRabbit with a vision to build an AI-first developer tooling
+company from the ground up. Building enterprise-ready applied AI tech is unlike
+any other software engineering challenge of the past. Based on our learnings
+while building complex workflows, it became apparent that we need to invest in a
+platform that can solve the following problems:
+
+- Prompt rendering: Prompt design and rendering is akin to responsive web
+  design. Web servers render pages based on the screen size and other
+  parameters, for example, on a mobile device, navigation bars are usually
+  rendered as hamburger menus, making it easier for human consumption.
+  Similarly, we need a prompt server that can render prompts based on the
+  context windows of underlying models and prioritize the packing of context
+  based on business attributes, making it easier for AI consumption. It's not
+  feasible to include the entire repository, past conversations, documentation,
+  learnings, etc. in a single code review prompt because of the context window
+  size limitations. Even if it was possible, AI models exhibit poor recall when
+  doing an inference on a completely packed context window. While tight packing
+  may be acceptable for use cases like chat, it’s not for use cases like code
+  reviews that require accurate inferences. Therefore, it's critical to render
+  prompts in such a way that the quality of inference is high for each use-case,
+  while being cost-effective and fast. In addition to packing logic, basic
+  guardrails are also needed, especially when rendering prompts based on inputs
+  from end-users. Since we provide a free service to public repositories, we
+  have to ensure that our product is not misused beyond its intended purpose or
+  tricked into divulging sensitive information, which could include our base
+  prompts.
+
+- Validation & quality checks: Generative AI models consume text and output
+  text. On the other hand, traditional code and APIs required structured data.
+  Therefore, the prompt service needs to expose a RESTful or gRPC API that can
+  be consumed by the other services in the workflow. We touched upon the
+  rendering of prompts based on structured requests in the previous point, but
+  the prompt service also needs to parse, validate responses into structured
+  data and measure the quality of the inference. This is a non-trivial problem,
+  and multiple tries are often required to ensure that the response is thorough
+  and meets the quality bar. For instance, we found that when we pack multiple
+  files in a single code review prompt, AI models often miss hunks within a file
+  or miss files altogether, leading to incomplete reviews.
+
+- Observability: One key challenge with generative AI and prompting is that it's
+  inherently non-deterministic. The same prompt can result in vastly different
+  outputs, which can be frustrating, but this is precisely what makes AI systems
+  powerful in the first place. Even slight variations in the prompt can result
+  in vastly inferior or noisy outputs, leading to a decline in user engagement.
+  At the same time, the underlying AI models are ever-evolving, and the
+  established prompts drift over time as the models get regular updates.
+  Traditional observability is of little use here, and we need to rethink how we
+  classify and track generated output and measure quality. Again, this is a
+  problem that we have to solve in-house.
+
+While FluxNinja's Aperture project was limited to solving a different problem
+around load management and reliability, we found that the underlying technology
+and the team's expertise were a perfect foundation for building the AI platform.
+Prompt engineering is in its nascent stage but is emerging as a joystick for
+controlling AI behavior. Packing the context window with relevant documents
+(retrieval augmented generation, aka RAG) is also emerging as the preferred way
+of providing proprietary data compared to fine-tuning the model. Most AI labs
+focus on increasing the context window rather than making fine-tuning easier or
+cheaper. Despite the emergence of these clear trends, applied AI systems are
+still in their infancy. None of the recent AI vendors seem to be building the
+"right" platform, as most of their focus has been on background/durable
+execution frameworks, model routing proxies/gateways, composable RAG pipelines,
+and so on. Most of these approaches fall short of what a real-world AI workflow
+requires. The right abstractions and best practices will still have to appear,
+and the practitioners themselves will have to build them. AI platforms will be a
+differentiator for AI-first companies, and we are excited to tackle this problem
+head-on with a systems engineering mindset.
+
+We are excited to have the FluxNinja team on board and to bring our users the
+best-in-class AI workflows. We are also happy to welcome
+[Harjot Gill](https://www.linkedin.com/in/harjotsgill/), the founder of
+FluxNinja, and the rest of the team to CodeRabbit.

docs/guides/prompt-customization.md

@@ -142,7 +142,7 @@ syntax node matches the rule or not. There are three kinds of atomic rule:
 
 #### Relational rule
 
-Relational rule defines the relationship between two syntax nodes. There are
+A relational rule defines the relationship between two syntax nodes. There are
 four kinds of relational rule: `inside`, `has`, `follows` and `precedes`.
 
 All four relational rules accept a sub-rule object as their value. The sub-rule