Lattice Semiconductor is the low power programmable leader, solving customer problems, and enabling designers to innovate across multiple applications
I don't see any actual reviews or detailed social mentions about Lattice in the content you've provided. The only mention appears to be a technical post about reducing LLM token usage, which doesn't seem directly related to user feedback about the Lattice software platform itself. To provide an accurate summary of what users think about Lattice (the performance management platform), I would need actual user reviews and social media discussions about the product's features, pricing, user experience, and overall satisfaction. Could you share the specific reviews and social mentions you'd like me to analyze?
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I don't see any actual reviews or detailed social mentions about Lattice in the content you've provided. The only mention appears to be a technical post about reducing LLM token usage, which doesn't seem directly related to user feedback about the Lattice software platform itself. To provide an accurate summary of what users think about Lattice (the performance management platform), I would need actual user reviews and social media discussions about the product's features, pricing, user experience, and overall satisfaction. Could you share the specific reviews and social mentions you'd like me to analyze?
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Cutting LLM token usage by 80% using recursive document analysis
When you employ AI agents, there’s a significant volume problem for document study. Reading one file of 1000 lines consumes about 10,000 tokens. Token consumption incurs costs and time penalties. Codebases with dozens or hundreds of files, a common case for real world projects, can easily exceed 100,000 tokens in size when the whole thing must be considered. The agent must read and comprehend, and be able to determine the interrelationships among these files. And, particularly, when the task requires multiple passes over the same documents, perhaps one pass to divine the structure and one to mine the details, costs multiply rapidly. **Matryoshka** is a tool for document analysis that achieves over 80% token savings while enabling interactive and exploratory analysis. The key insight of the tool is to save tokens by caching past analysis results, and reusing them, so you do not have to process the same document lines again. These ideas come from recent research, and retrieval-augmented generation, with a focus on efficiency. We'll see how Matryoshka unifies these ideas into one system that maintains a persistent analytical state. Finally, we'll take a look at some real-world results analyzing the [anki-connect](https://git.sr.ht/~foosoft/anki-connect) codebase. --- ## The Problem: Context Rot and Token Costs A common task is to analyze a codebase to answers a question such as “What is the API surface of this project?” Such work includes identifying and cataloguing all the entry points exposed by the codebase. **Traditional approach:** 1. Read all source files into context (~95,000 tokens for a medium project) 2. The LLM analyzes the entire codebase’s structure and component relationships 3. For follow-up questions, the full context is round-tripped every turn This creates two problems: ### Token Costs Compound Every time, the entire context has to go to the API. In a 10-turn conversation about a codebase of 7,000 lines, almost a million tokens might be processed by the system. Most of those tokens are the same document contents being dutifully resent, over and over. The same core code is sent with every new question. This redundant transaction is a massive waste. It forces the model to process the same blocks of text repeatedly, rather than concentrating its capabilities on what’s actually novel. ### Context Rot Degrades Quality As described in the [Recursive Language Models](https://arxiv.org/abs/2505.11409) paper, even the most capable models exhibit a phenomenon called context degradation, in which their performance declines with increasing input length. This deterioration is task-dependent. It’s connected to task complexity. In information-dense contexts, where the correct output requires the synthesis of facts presented in widely dispersed locations in the prompt, this degradation may take an especially precipitous form. Such a steep decline can occur even for relatively modest context lengths, and is understood to reflect a failure of the model to maintain the threads of connection between large numbers of informational fragments long before it reaches its maximum token capacity. The authors argue that we should not be inserting prompts into the models, since this clutters their memory and compromises their performance. Instead, documents should be considered as **external environments** with which the LLM can interact by querying, navigating through structured sections, and retrieving specific information on an as-needed basis. This approach treats the document as a separate knowledge base, an arrangement that frees up the model from having to know everything. --- ## Prior Work: Two Key Insights Matryoshka builds on two research directions: ### Recursive Language Models (RLM) The RLM paper introduces a new methodology that treats documents as external state to which step-by-step queries can be issued, without the necessity of loading them entirely. Symbolic operations, search, filter, aggregate, are actively issued against this state, and only the specific, relevant results are returned, maintaining a small context window while permitting analysis of arbitrarily large documents. Key point is that the documents stay outside the model, and only the search results enter the context. This separation of concerns ensures that the model never sees complete files, instead, a search is initiated to retrieve the information. ### Barliman: Synthesis from Examples [Barliman](https://github.com/webyrd/Barliman), a tool developed by William Byrd and Greg Rosenblatt, shows that it is possible to use program synthesis without asking for precise code specifications. Instead, input/output examples are used, and a solver engine is used as a relational programming system in the spirit of [miniKanren](http://minikanren.org/). Barliman uses such a system to synthesize functions that satisfy the constraints specified. The system interprets the examples as if they were relational rules, and the synthesis e
View originalCutting LLM token usage by 80% using recursive document analysis
When you employ AI agents, there’s a significant volume problem for document study. Reading one file of 1000 lines consumes about 10,000 tokens. Token consumption incurs costs and time penalties. Codebases with dozens or hundreds of files, a common case for real world projects, can easily exceed 100,000 tokens in size when the whole thing must be considered. The agent must read and comprehend, and be able to determine the interrelationships among these files. And, particularly, when the task requires multiple passes over the same documents, perhaps one pass to divine the structure and one to mine the details, costs multiply rapidly. **Matryoshka** is a tool for document analysis that achieves over 80% token savings while enabling interactive and exploratory analysis. The key insight of the tool is to save tokens by caching past analysis results, and reusing them, so you do not have to process the same document lines again. These ideas come from recent research, and retrieval-augmented generation, with a focus on efficiency. We'll see how Matryoshka unifies these ideas into one system that maintains a persistent analytical state. Finally, we'll take a look at some real-world results analyzing the [anki-connect](https://git.sr.ht/~foosoft/anki-connect) codebase. --- ## The Problem: Context Rot and Token Costs A common task is to analyze a codebase to answers a question such as “What is the API surface of this project?” Such work includes identifying and cataloguing all the entry points exposed by the codebase. **Traditional approach:** 1. Read all source files into context (~95,000 tokens for a medium project) 2. The LLM analyzes the entire codebase’s structure and component relationships 3. For follow-up questions, the full context is round-tripped every turn This creates two problems: ### Token Costs Compound Every time, the entire context has to go to the API. In a 10-turn conversation about a codebase of 7,000 lines, almost a million tokens might be processed by the system. Most of those tokens are the same document contents being dutifully resent, over and over. The same core code is sent with every new question. This redundant transaction is a massive waste. It forces the model to process the same blocks of text repeatedly, rather than concentrating its capabilities on what’s actually novel. ### Context Rot Degrades Quality As described in the [Recursive Language Models](https://arxiv.org/abs/2505.11409) paper, even the most capable models exhibit a phenomenon called context degradation, in which their performance declines with increasing input length. This deterioration is task-dependent. It’s connected to task complexity. In information-dense contexts, where the correct output requires the synthesis of facts presented in widely dispersed locations in the prompt, this degradation may take an especially precipitous form. Such a steep decline can occur even for relatively modest context lengths, and is understood to reflect a failure of the model to maintain the threads of connection between large numbers of informational fragments long before it reaches its maximum token capacity. The authors argue that we should not be inserting prompts into the models, since this clutters their memory and compromises their performance. Instead, documents should be considered as **external environments** with which the LLM can interact by querying, navigating through structured sections, and retrieving specific information on an as-needed basis. This approach treats the document as a separate knowledge base, an arrangement that frees up the model from having to know everything. --- ## Prior Work: Two Key Insights Matryoshka builds on two research directions: ### Recursive Language Models (RLM) The RLM paper introduces a new methodology that treats documents as external state to which step-by-step queries can be issued, without the necessity of loading them entirely. Symbolic operations, search, filter, aggregate, are actively issued against this state, and only the specific, relevant results are returned, maintaining a small context window while permitting analysis of arbitrarily large documents. Key point is that the documents stay outside the model, and only the search results enter the context. This separation of concerns ensures that the model never sees complete files, instead, a search is initiated to retrieve the information. ### Barliman: Synthesis from Examples [Barliman](https://github.com/webyrd/Barliman), a tool developed by William Byrd and Greg Rosenblatt, shows that it is possible to use program synthesis without asking for precise code specifications. Instead, input/output examples are used, and a solver engine is used as a relational programming system in the spirit of [miniKanren](http://minikanren.org/). Barliman uses such a system to synthesize functions that satisfy the constraints specified. The system interprets the examples as if they were relational rules, and the synthesis e
View originalLattice uses a tiered pricing model. Visit their website for current pricing details.
Key features include: Industrial Solution, Solution Stacks, Automotive, Factory Automation, Other Industrial, Client Computing, Datacenter Systems, Wireless.
Lattice is commonly used for: Industrial Solution, Solution Stacks, Partner Type.
Based on user reviews and social mentions, the most common pain points are: token cost, token usage.
