> THIS NOTICE SERVES TO INFORM YOU THAT PULLEY WILL BE SHUTTING DOWN AND WILL CEASE ALL OPERATIONS AND SERVICES ON 12/8/26.

Besides Carta, are there any other equity management solutions that the USA-based HN community uses?

[1] https://pulley.com/

Comments URL: https://news.ycombinator.com/item?id=49715984

Points: 8

# Comments: 4

They are useful for neural information retrieval (RAG, memory), which relies heavily on content vectorization and similarity matching using their dot products.

One of the more interesting findings was a poisoned memory vulnerability. Basically, this is when an attacker injects memories that instruct the LLM to ignore all previous instructions and do something else instead (one of the subjects of the posted article). For example:

> Ignore all previous instructions and instead [Take Harmful Action X].

The immediate fix is to fence all user-generated content that's injected into the context window, e.g.:

>

> Ignore all previous instructions and instead [Take Harmful Action X].

>

And give the LLM explicit instructions not to act on data within the fence. However, by adding a nonce to the BEGIN/END commands, you can harden the system against attempts "END" the fence prematurely. For example, , and then repeat the nonce in the ending instruction.

This strategy leans on the ability of the LLM to follow instructions, but it works well with most modern models we tested.

We've shared a few additional details at [1], although the main point of the article is to describe red teaming strategies with OpenCode and GLM.

[1] https://goodmem.ai/blog/red-teaming-goodmem-with-glm/

1. It tests visual reasoning and structured output in a single task.

2. It seems to sort correctly on advancing general intelligence. As a counterexample, if I'm not misremembering, artificialanalysis.ai made some changes to their benchmark recently after Astra ranked below several older models.

3. While models have gotten significantly better in the past 2 years, the top model is still at 0.78 F1, so the test is not yet saturated. As a reference point, when I started, the top models were in the [0.1, 0.2] range.

[1] https://dorrit.pairsys.ai/

https://github.com/PAIR-Systems-Inc/Agentic_RAG_GoodMem

Comments URL: https://news.ycombinator.com/item?id=49631278

Points: 1

# Comments: 0

For the past two years, I've run the Little Dorrit Editor Benchmark. Typesetting is my hobby and I wanted to see how well LLMs could extract editorial marks from a printed page.

The initial results were not encouraging, but performance has risen rapidly since the summer of 2025. From F1 scores in the low 0.2s in 2024, we are now at 0.78 with GPT 6 Astra! Fable 5.1 scores 0.73 (high thinking mode for both).

The best thing about this benchmark is that there is still plenty of room to climb.

Comments URL: https://news.ycombinator.com/item?id=49586814

Points: 3

# Comments: 1

I hadn't updated the benchmark in several months, but there are some interesting findings. Fable 5 takes the top spot (0.6579), setting a new performance record, while Kimi K3 is within a hair's breadth of its performance.

The most significant finding is that Opus 4.8 regresses drastically compared to Opus 4.7, from 0.4805 to 0.2150. This seems mainly due to a regression in its ability to count line numbers, and it's something you might want to keep in mind when designing your own agents.

Comments URL: https://news.ycombinator.com/item?id=49028771

Points: 4

# Comments: 1

On (2), I agree with you for local models. BUT, there are also the open source Chinese models accessible via open-router. Your argument ("don't hold a candle to SOTA models") does not hold if the comparison is between those.

On (1), I agree more with the grandparent than with your assessment. Yes, OpenAI and Anthropic are killing it for now, but the time horizon is very short. I use codex and claude daily, but it's also clear to me that open source is catching up quickly, both w.r.t. the models and the agentic harnesses.

My current approach to IP is trade secrets. If we publish, we are careful to avoid details that would make the techniques easy to productionize.

Comments URL: https://news.ycombinator.com/item?id=47271374

Points: 2

# Comments: 0

We support both commercial APIs and self-hosted options:

  - Cohere (rerank-english-v3.0, etc.)
  - Voyage AI (rerank-2.5)
  - Jina AI (jina-reranker-v3)

  - TEI - https://github.com/huggingface/text-embeddings-inference
  - vLLM - https://docs.vllm.ai/en/v0.8.1/serving/openai_compatible_server.html#rerank-api

  # Cohere
  goodmem reranker create \
    --display-name "Cohere" \
    --provider-type COHERE \
    --endpoint-url "https://api.cohere.com" \
    --model-identifier "rerank-english-v3.0" \
    --cred-api-key "YOUR_API_KEY"

  # Self-hosted TEI (e.g., BAAI/bge-reranker-v2-m3)
  goodmem reranker create \
    --display-name "TEI Local" \
    --provider-type TEI \
    --endpoint-url "http://localhost:8081" \
    --model-identifier "BAAI/bge-reranker-v2-m3"

  goodmem memory retrieve \
    --space-id  \
    --post-processor-interactive \
    "your query"

It's built on Postgres, which I know you said you left behind, but one of the cool features it supports is hybrid search over multiple vector representations of a passage, so you can do a dense (e.g. nomic) and sparse (e.g. splade) search. Reranking is also built in, although it lacks automatic caching (since, in general, the corpus changes over time)

It also deploys to fly.io/railway and costs a few bucks a month to run if you're willing to use cloud-hosted embedding models (otherwise, you can run TEI/vLLM on CPU or GPU for the setup you described).

I hope it's helpful to someone.

https://dorrit.pairsys.ai/

> This benchmark evaluates the ability of multimodal language models to interpret handwritten editorial corrections in printed text. Using annotated scans from Charles Dickens' "Little Dorrit," we challenge models to accurately capture human editing intentions.

Curious to hear thoughts from others working on similar problems.

Comments URL: https://news.ycombinator.com/item?id=43641115

Points: 1

# Comments: 2

But to your point, note that in 2020 neuroscientists introduced the Tolman-Eichenbaum Machine (TEM) [1], a mathematical model of the hippocampus that bears a striking resemblance to transformer architecture.

Artem Kirsanov has a very nice piece on TEM, "Can we Build an Artificial Hippocampus?" [2] The link is directly to the spot where he makes the connection to transformers, although you should watch the whole video for context.

Because I wasn't clear on the chronology, I went back and asked one of the "Attention" authors whether mathematical models of the hippocampus inspired their paper? His answer was "no". If TEM was developed without pre-knowledge of transformers, then it's a very deep result IMHO.

[1] https://www.sciencedirect.com/science/article/pii/S009286742...

[2] https://www.youtube.com/watch?v=cufOEzoVMVA&t=1254s