SlideBySide – Present slides without sharing your screen
Why is it useful? - Present slides without sharing the screen and have every one be on the same page. - Present together with other cohosts and all control the current page. - Other features such as presentation collab features.Why should I not just vibe code my own solution? - Its a hassle to maintain a version online.
FL score
out of 100
Verdict
high confidence
Competition
No competitor data yet
Trend
No signal yet
A collaborative slide presentation tool that avoids screen sharing by syncing slides across participants.
The pain
The gap
Build angle
Strengths
- Clear user pain around screen sharing limitations
- Collaborative multi-host control is a differentiator
- Potential for integration with existing presentation platforms
Risks
- Uncertain willingness to pay for a niche feature
- Competition from established video conferencing and presentation tools
- User inertia and adoption barriers
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
- 65
- Willingness to pay
- 60
- Buildability
- 70
The problem of screen sharing limitations is clear but the solution space is moderately crowded and willingness to pay is uncertain.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
The idea addresses a real pain but the value and pricing model are not fully validated, limiting its immediate appeal.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
There is a decent market fit but competition and unclear monetization reduce the overall opportunity score.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
The concept is feasible but lacks strong differentiation and clear customer acquisition strategy.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
The product solves a niche problem but faces challenges in scaling and convincing users to switch from existing tools.
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!
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Postgres extension for BM25 relevance-ranked full-text search
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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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