Releases·Tool Release
I shrank a 4,000 token system prompt to 300
A developer describes shrinking a bloated 4,000-token system prompt to about 300 tokens by moving mutable facts out of the prompt and into Synap, a tool that extracts and updates facts during conversation. The change reportedly improved coherence past message thirty. For practitioners, it illustrates treating system prompts as static rule carriers and offloading changing state to retrieval.
10 engagement1 sourceTue, Sep 29, 2026, 02:46 PM
AI Overview
- A developer cut a 4,000-token system prompt to ~300 by moving volatile facts to Synap, which extracts and supersedes facts mid-conversation
- Synap claims 92% on a standardised LongMemEval run vs 57.5 for the closest widely used alternative, with ~15 ms retrieval latency
- Synap's free tier offers 5,000 credits with GitHub sign-in
Entities
Related
Social·Community
Bigger context windows just give you a bigger dead zone in the middle
A developer on r/LocalLLaMA tracked 847 agent runs and measured instruction-following drop from 94% to 41% as the context window filled, holding steady then falling off a cliff. A follow-up post attributes the drop to U-shaped attention ignoring the middle of long contexts, warns that aggressive compaction can drop critical details, and describes a retrieval-based alternative (Synap) that ranks memories within a token budget, runs under 15ms at P75, and roughly halves token spend in production workloads.
A developer on r/LocalLLaMA tracked 847 agent runs and measured instruction-following drop from 94% to 41% as the context window filled, holding steady then falling off a cliff. A follow-up post attributes the drop to U-shaped attention ignoring the middle of long contexts, warns that aggressive compaction can drop critical details, and describes a retrieval-based alternative (Synap) that ranks memories within a token budget, runs under 15ms at P75, and roughly halves token spend in production workloads.
Updated Tue, Sep 22, 2026, 04:08 PM — Author of the 847-run analysis follow-up discloses they work on Synap, a retrieval-based memory tool claiming ~50% token
33 engagement1 sourceredditTue, Sep 22, 2026, 04:08 PM
Social·Community
Maximem engineer says one memory extraction config across agent types quietly degrades all of them
A Maximem engineer describes how shipping one memory extraction config across agent types produced mediocre memory for all agents. The post lays out how support versus sales agents need different extraction rules, memory-type weighting, decay windows, and retrieval ranking, and describes generating these configs from agent descriptions in Synap.
A Maximem engineer describes how shipping one memory extraction config across agent types produced mediocre memory for all agents. The post lays out how support versus sales agents need different extraction rules, memory-type weighting, decay windows, and retrieval ranking, and describes generating these configs from agent descriptions in Synap.
25 engagement1 sourceredditTue, Sep 15, 2026, 05:04 PM
Social·Community
You're leaking data if your agent memory uses post filter tenant scoping
An engineer at Maximem (Synap) details how post-filter tenant scoping in agent memory systems leaks data at retrieval time, before any prompt rule applies. The writeup catalogs three failure paths (post-filter vector search, unscoped graph traversal, missing tenant in cache keys) and describes the store-level scoping model they adopted, including write-time scope enforcement, tenant-local entity resolution, and partition-based deletion.
An engineer at Maximem (Synap) details how post-filter tenant scoping in agent memory systems leaks data at retrieval time, before any prompt rule applies. The writeup catalogs three failure paths (post-filter vector search, unscoped graph traversal, missing tenant in cache keys) and describes the store-level scoping model they adopted, including write-time scope enforcement, tenant-local entity resolution, and partition-based deletion.
22 engagement1 sourceredditWed, Sep 16, 2026, 12:48 PM
Products·launched
Maximem Synap — persistent memory SDK for AI agents
Maximem Synap is a memory SDK that gives AI agents persistent memory across sessions. It uses LLM-based typed extraction and entity resolution to store user preferences and context so agents stop re-asking known information. It targets developers building agents who lose context between sessions.
Maximem Synap is a memory SDK that gives AI agents persistent memory across sessions. It uses LLM-based typed extraction and entity resolution to store user preferences and context so agents stop re-asking known information. It targets developers building agents who lose context between sessions.
37 engagement1 sourceredditSat, Sep 19, 2026, 01:44 PM
Social·Community
Scoping memory at session level instead of customer level is an architecture mistake.
A Maximem memory-infrastructure engineer describes a common failure in support-agent memory: scoping stores by session or channel ID, so chat, email, and phone interactions for the same customer are never linked. The post proposes entity-resolution at write time and a customer-level scope hierarchy so every channel reads the same memory.
A Maximem memory-infrastructure engineer describes a common failure in support-agent memory: scoping stores by session or channel ID, so chat, email, and phone interactions for the same customer are never linked. The post proposes entity-resolution at write time and a customer-level scope hierarchy so every channel reads the same memory.
27 engagement1 sourceredditMon, Sep 21, 2026, 02:34 PM