Makers of Hindsight. Agent memory that lets your agents learn over time. https://xitter.cf/t.co/JkzXgOQg74
Boulder, CO
Joined December 2023
- Tweets1.4K
- Following331
- Followers1.5K
- Likes950
Vectorize retweeted
DOT agent just orchestrated a custom plugin to give my phone chatgpt shared memory with @Vectorizeio !!!
xitter.cf/stevenmathern/status/2…
chatGPT work was able to create a custom plug-in for ChatGPT mobile to connect to the hindsight memory system @Vectorizeio as you can see in the screenshots and we have confirmed that it is functional. This was orchestrated by the dot agent running two different chats said it's sol high. It is now assisting Claude in connecting to the shared memory bank while I just talk on my phone, only occasionally clicking a confirmation on the Mac.
Vectorize retweeted
Part 69 in amazing repos
Agent memory that learns
The diagram shows the retain, recall and reflect loop
Repo: github.com/vectorize-io/hind…
Vectorize retweeted
Asked Hermes to consult my Hindsight memory first. Its verdict: “You have a technician’s nose for bullshit, an architect’s appetite for interconnected systems, and the browsing restraint of a raccoon with an all-access badge.” Lmao
Vectorize retweeted
chatGPT work was able to create a custom plug-in for ChatGPT mobile to connect to the hindsight memory system @Vectorizeio as you can see in the screenshots and we have confirmed that it is functional. This was orchestrated by the dot agent running two different chats said it's sol high. It is now assisting Claude in connecting to the shared memory bank while I just talk on my phone, only occasionally clicking a confirmation on the Mac.
8 Agent Memory Use Cases (With Real Examples).
"Agent memory" sounds abstract until you notice it is the same fix applied to a dozen different problems. Any time an agent should remember something past the current session, and today starts from a blank slate, that is a memory-shaped hole. Hindsight fills it with the same three operations everywhere: retain to write, recall to read, reflect to reason. What changes from one use case to the next is not the primitive, it is who the memory belongs to and where it surfaces.
Here are the agent memory use cases people actually build, each with a concrete example: lnkd.in/gqe9TWR2
Vectorize retweeted
Memory should reduce repetition, not replace verification. We pair Hermes + Hindsight with live-source checks. How it works: tevpro.com/blog/how-we-use-h…
Vectorize retweeted
Turning Jev's typed answers into an ordering for candidate memories is a smart fit for Hindsight. hevmind.com/writing/jev-has-…
We Added Jev as a Reranker. Here's What We Learned.
Hindsight 0.10.1 adds Jev as a reranker provider. Jev is an unusual model: it produces no text at all. You send it a passage and a typed question, and you get back a typed answer with a calibrated probability. TypeSafe calls this class of thing a System One model, after Kahneman's fast, automatic kind of thinking.
That shape is a good fit for reranking, which is not a writing task. There is a pile of candidate memories and a question about which ones matter, and the only thing you want back is an ordering.
Getting there took longer than we expected, and two of the designs we tried failed outright. Here is what we learned.
Know more: linkedin.com/pulse/we-added-…
Fastest growing agent repo in October: Hindsight
10 repos de GitHub para agentes IA que más han crecido en Octubre:
1. Hindsight
Memoria para tus agentes que aprende de lo que hacen.
+28.5K estrellas en el último mes.
github.com/vectorize-io/hind…
2. Paperclip
Una app para gestionar a todos tus agentes en el trabajo, como si fueran un equipo.
+24.8K, ya pasa de 105.9K.
github.com/paperclipai/paper…
3. ECC
Skills, memoria y seguridad para Claude Code, Codex, OpenCode y Cursor.
+22.5K, va por 286K.
github.com/affaan-m/ECC
4. Impeccable
Un lenguaje de diseño para que lo que diseña tu agente no parezca hecho por una IA.
+15.1K.
github.com/pbakaus/impeccabl…
5. FreeLLMAPI
34 proveedores gratis y 635 modelos detrás de un solo endpoint. Si uno cae, salta al siguiente.
+11.2K.
github.com/tashfeenahmed/fre…
6. OpenShell de NVIDIA
Un entorno seguro y privado donde dejar trabajar a tus agentes.
+8.3K.
github.com/NVIDIA/OpenShell
7. Plugins oficiales de Claude Code
El directorio de plugins que mantiene la propia Anthropic.
+7.2K.
github.com/anthropics/claude…
8. CLI-Anything
Convierte cualquier programa en algo que tu agente puede manejar.
+6.4K.
github.com/HKUDS/CLI-Anythin…
9. text-to-cad
Tu agente diseña piezas en 3D a partir de texto.
+6.1K.
github.com/earthtojake/text-…
10. Knowledge Work Plugins de Anthropic
Plugins para usar Claude en ventas, legal, finanzas y marketing.
+5.8K.
github.com/anthropics/knowle…
Todos open source y gratis.
We Added Jev as a Reranker. Here's What We Learned.
Hindsight 0.10.1 adds Jev as a reranker provider. Jev is an unusual model: it produces no text at all. You send it a passage and a typed question, and you get back a typed answer with a calibrated probability. TypeSafe calls this class of thing a System One model, after Kahneman's fast, automatic kind of thinking.
That shape is a good fit for reranking, which is not a writing task. There is a pile of candidate memories and a question about which ones matter, and the only thing you want back is an ordering.
Getting there took longer than we expected, and two of the designs we tried failed outright. Here is what we learned.
Know more: linkedin.com/pulse/we-added-…
Document search used to match only the document id. It now searches document text and tells you which fields matched, so the search box finds a document by what it actually says.
Knowledge pages and mental models also download as PDF or Markdown.
Most of this release is reliability work:
→ deadlocks between consolidation, retain and delete
→ large graph reads freezing the API
→ retains replayed after an LLM key was rejected
→ a failed chunk replacing a stored document
→ workers exiting cleanly on one SIGTERM
Read the blog here: hindsight.vectorize.io/blog/…
And the changelog: hindsight.vectorize.io/chang…
Banks also get default_observation_scopes, used for every retain that sends none.
Set it once and memories about the same subject from different agents consolidate together, with no client change.
PATCH an existing document and only the memories whose scopes move get rebuilt.
Observations are rebuilt, not versioned. Edit a fact and everything built on it is deleted and rebuilt.
That used to leave no trace. Each document now records what changed and which rebuild it caused, so you can work out later why an observation looks the way it does.