How do you deal with AI output of your boss?
So, my boss goes on a rampage. You can clearly see the joy and at the same time the rage and impatience. AI is the tool he always wanted. We are too slow. Process is out of the window. No need for sprints anymore. No planning, just doing. All people downstream must somehow adapt.Whenever i see now his automated messages in slack, my natural reaction is: why do i even bother reading that? Thats for machines to read. He does not need me? Clearly i am not made for that. What is he expecting me to do? To confirm how great the AI did the job? Should i remember all these perfect presentations AI has created and integrate it into my doings into the coming weeks?I am torn. Should i really free my AI and deal with all this incoming text? Is this the way to react on this perfection?I am not mad at him, it clearly frees him of all the burder of dealing with people.I just dont know how to deal with this output personally. It is not for humans.I feel left alone. This is not a company anymore. Everyone is talking to AI now. This feels like chaos. Where everyone can do everything and stepping into each other's domain and it is perfect. The only way AI is stopped at are the ACLs.And because AI does everything for me, i don't remember things anymore, as i have no emotion connected with it. Feels like bad schooling and memoizing and get through it and you can't as it so dry and lifeless.So, how people deal with it in a company context? How have they setup their machines to react and still be up to date without reading the constant stream of changes coming from others?
FL score
out of 100
Verdict
high confidence
Competition
No competitor data yet
Trend
No signal yet
Helping employees manage and adapt to overwhelming AI-generated communication in the workplace.
The pain
The gap
Build angle
Strengths
- Addresses a growing issue as AI adoption increases in workplaces
- Focuses on human factors often overlooked in AI integration
- Potential to improve communication efficiency and employee morale
Risks
- Unclear who would pay for such a solution or if companies see it as a priority
- Difficult to build a universally effective filter given diverse company cultures and AI uses
- Risk of becoming redundant as AI tools evolve to better communicate with humans
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 75
- Solution gap
- 55
- Willingness to pay
- 50
- Buildability
- 70
The problem of human workers feeling disconnected and overwhelmed by AI-driven communication is clear, but the solution space is vague and monetization uncertain.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
The idea addresses a real pain point but lacks a clear, scalable solution and a defined customer willing to pay for it.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
There is a recognizable problem with AI output overload in companies, but the concept needs refinement to identify a viable product and market fit.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
The concept highlights a cultural and operational challenge in AI adoption, but it is more of an observation than a concrete business opportunity yet.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
The idea touches on a genuine workplace friction caused by AI, but it needs a sharper focus on a specific user problem and a clear path to product development.
Five lenses, one composite. How scoring works
The angle
No market research recorded for this idea yet.
Semble – Code search for agents that uses 98% fewer tokens than grep
Hey HN! We (Stephan and Thomas) recently open-sourced Semble. We kept running into the same problem while using Claude Code on large codebases: when the agent can't find something directly, it falls back to grep, reading full files or launching subagents. This uses a lot of tokens, and often still misses the relevant code. There are existing tools for this, but they were either too slow to index on demand, needed API keys, or had poor retrieval quality.Semble is our solution for this. It combines static Model2Vec embeddings (using our latest static model: potion-code-16M) with BM25, fused via RRF and reranked with code-aware signals. Everything runs on CPU since there's no transformers involved. On our benchmark of ~1250 query/document pairs across 63 repos and 19 languages, it uses 98% fewer tokens than grep+read and reaches 99% of the retrieval quality of a 137M-parameter code-trained transformer, while being ~200x faster.Main features:- Token-efficient: 98% fewer tokens than grep+read- Fast: ~250ms to index a typical repo on our benchmark, ~1.5ms per query on CPU (very large repos may take longer)- Accurate: 0.854 NDCG@10, 99% of the best transformer setup we tested- MCP server: drop-in for Claude Code, Cursor, Codex, OpenCode- Zero config: no API keys, no GPU, no external servicesInstall in Claude Code with: claude mcp add semble -s user -- uvx --from "semble[mcp]" sembleOr check our README for other installation instructions, benchmarks, and methodology:Semble: https://github.com/MinishLab/sembleBenchmarks: https://github.com/MinishLab/semble/tree/main/benchmarksModel: https://huggingface.co/minishlab/potion-code-16MLet us know if you have any feedback or questions!
AI
Postgres extension for BM25 relevance-ranked full-text search
Last summer we faced a conundrum at my company, Tiger Data, a Postgres cloud vendor whose main business is in timeseries data. We were trying to grow our business towards emerging AI-centric workloads and wanted to provide a state-of-the-art hybrid search stack in Postgres. We'd already built pgvectorscale in house with the goal of scaling semantic search beyond pgvector's main memory limitations. We just needed a scalable ranked keyword search solution too.The problem: core Postgres doesn't provide this; the leading Postgres BM25 extension, ParadeDB, is guarded behind AGPL; developing our own extension appeared daunting. We'd need a small team of sharp engineers and 6-12 months, I figured. And we'd probably still fall short of the performance of a mature system like Parade/Tantivy.Or would we? I'd be experimenting long enough with AI-boosted development at that point to realize that with the latest tools (Claude Code + Opus) and an experienced hand (I've been working in database systems internals for 25 years now), the old time estimates pretty much go out the window.I told our CTO I thought I could solo the project in one quarter. This raised some eyebrows.It did take a little more time than that (two quarters), and we got some real help from the community (amazing!) after open-sourcing the pre-release. But I'm thrilled/exhausted today to share that pg_textsearch v1.0 is freely available via open source (Postgres license), on Tiger Data cloud, and hopefully soon, a hyperscalar near you:https://github.com/timescale/pg_textsearchIn the blog post accompanying the release, I overview the architecture and present benchmark results using MS-MARCO. To my surprise, we were not only able to meet Parade/Tantivy's query performance, but exceed it substantially, measuring a 4.7x advantage on query throughput at scale:https://www.tigerdata.com/blog/pg-textsearch-bm25-fu
AI
Consus (YC F24) – LLM gateway for companies handling sensitive data
Hey Hacker News! I'm Eric, the founder of Consus. We route all AI requests to ITAR restricted and controlled unclassified information (CUI) compliant infrastructure. Consus itself satisfies ITAR restrictions, and we recently completed our FedRAMP equivalency audit against the 410 FedRAMP High controls. FedRAMP High is the U.S. government’s strictest security baseline for cloud services handling sensitive unclassified data. A breach of this data could cause severe or catastrophic harm. Achieving FedRAMP equivalency, or authorization, is required before processing CUI information.Demo: https://youtu.be/6MYxJe6P4oYAerospace and defense is experiencing rapid growth. Unfortunately, most companies are struggling to use AI because of necessary data protections & compliance risk. These companies can move even faster if they could use the AI tools that most other industries take for granted.I spent most of my career prior to Consus in aerospace & defense. I launched national security payloads at SpaceX, and was an early employee at Castelion. I’ve watched first hand how compliance red-tape makes it difficult for companies to adopt new technologies and trust vendors that touch their sensitive data. For example, I worked with a satellite manufacturer that doesn’t allow any AI tools, even for non-sensitive work.Even as AI has become extremely useful over the past 9-12 months, I was surprised by how many aerospace & defense companies refused to touch it. They could see how it could be helpful, but they were scared about model providers training on their data, violating an ITAR rule, or risk losing new business because of a failed cyber assessment. These are all rational fears.There were a lot of ways to solve some/all of these problems, but we focused on building a compliant AI inference layer that can "plug" into any AI tool or workflow. Just as OpenRouter integrates with most tools, Consus can connect to tools like ChatGPT or Clau
AI
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