This is very true but I think there is an incredibly long tail of people who "can't survive a terminal session" and I actually question if a terminal ui will win out long term.
1,241 karma · joined August 14, 2013
Having the feature table pivoted (with 200 feature1, feature2, etc columns) meant they had to do meta queries to system.columns to get all the feature columns which made the query sensitive to permissioning changes (especially duplicate databases).
A Crowdstrike style config update that affects all nodes but obviously isn't tested in any QA or staged rollout strategy beforehand (the application panicking straight away with this new file basically proves this).
Finally an error with bot management config files should probably disable bot management vs crash the core proxy.
I'm interested here why they even decided to name Clickhouse as this error could have been caused by any other database. I can see though the replicas updating causing flip / flopping of results would have been really frustrating for incident responders.
When looking at multi-vector / ColBERT style approaches, the embedding per token approach can massively increase costs. You might go from a single 768 dimension vector to 128 x 130 = 16,640 dimensions. Even with better results from a multi-vector model this can make it unfeasible for many use-cases.
Muvera, converts the multiple vectors into a single fixed dimension (usually net smaller) vector that can be used by any ANN index. As you now have a single vector you can use all your existing ANN algorithms and stack other quantization techniques for memory savings. In my opinion it is a much better approach than PLAID because it doesn't require specific index structures or clustering assumptions and can achieve lower latency.
As a parent I do share concerns for short duration video content like Tiktok, Reels, and Youtube shorts etc, but I think any sensible regulation there would be better suited to everyone.
Age verification has such terrible consequences to anonymity that it shouldn't be an option. From the Explanatory Memorandum it looks like the current eSafety Commissioner was involved which unfortunately explains a lot.
If open models continue to keep up and industry as a whole just keeps improving on benchmarks fairly slowly and evenly (a big assumption but what has been happening over past year), then you would assume valuations would eventually drop. Saying that there is still a lot of easy value left on the table (great voice integration etc) and a lot of room for innovations to be incorporated in other industries.
Additionally each foundation LLM provider has a non-zero chance of a large step change in performance. If they can keep the value of this step change it would have huge upsides but this is also hard to reason about (what is the valuation of private companies and the share market if AGI exists - does this even matter anymore).
1. gRPC. Using gRPC to write vectors has had a really nice performance boost. It is released in Weaviate core but here is still some work on do on the clients. Feel free to get in contact if you would like to try it out.
2. Parameter tuning. lowering `efConstruction` can speed up imports.
3. We are also working on async indexing https://github.com/weaviate/weaviate/issues/3463 which will further speed things up.
In comparison with pgvector, Weaviate has more flexible query options such as hybrid search and quantization to save memory on larger datasets.
- Using two separate systems for traditional BM25 and vector search and keeping them in-sync is pretty difficult from an operations perspective. A combined system is much easier to manage and will have better end-to-end latency.
- For combining scores, a linear combination like this article suggests is not recommended and instead rank fusion https://rodgerbenham.github.io/bc17-adcs.pdf (where you care what each method ranks first rather than the absolute score) is used.
- The point of adding both search methods is for dealing with what researchers term "out of domain data". This is for datasets the model producing the vectors was not trained on. Research from Google https://arxiv.org/abs/2201.10582 suggests hybrid search with rank fusion helps in this case by around 20.4%. For "in domain" data, the model (usually transformer based) will out perform BM25.
- Using a cross encoder [2] is a good component to add to improve relevance. It will just though rerank the final results, so if the initial search returns 100 garbage results the cross encoder won't be able to help.
[1] https://weaviate.io/blog/hybrid-search-explained [2] https://www.sbert.net/examples/applications/cross-encoder/RE...
1. If you are self-hosting, you have now segmented yourself into the customer category with the government, military, and large corporates concerned about security. This segment will pay more and so will be charged more. In an enterprise software company the vast majority of revenue comes from the larger customers vs many small customers, so these are the users that pricing gets optimised for.
2. Gitlab wants to be seen in the market as a SaaS company as subscription revenue is far preferable to licence revenue from a churn perspective, and also from a general trend perspective where self-hosted, hard to maintain solutions get replaced by cloud solutions.
Disclaimer I used to work at Elastic!
- Used Logstash instead of Beats for simple task of reading syslog json data. Beats (https://www.elastic.co/guide/en/beats/filebeat/current/fileb...) would have performed better especially around resource usage.
- Set very low Logstash heap of 256mb https://github.com/SigNoz/logs-benchmark/blob/0b2451e6108d8f...
- Added grok processor https://github.com/SigNoz/logs-benchmark/blob/0b2451e6108d8f... Dissect is faster here
- No index template configuration This would cause higher disk usage than needed due to duplicate mappings. Again a Logstash vs Beats thing. For this test more primary shards and a larger refresh interval would also improve things.
- Graph complaining Elasticsearch using 60% available memory. This is as configured, they could use less with not much impact to performance.
- Document counts do not match.. This is probably due to using syslog with random generated data vs creating a test dataset on disk and reading the same data into all platforms.
- Aggregation queries were not provided in repo https://github.com/SigNoz/logs-benchmark so cannot validate.
I'm actually surprised Elastic did so well in this benchmark given the misconfiguration.
1. Ubiquiti ER-X
2. Ubiquiti AP Lite x 2 (upstairs and downstairs)
3. Inbuilt Cat6 cabling in house
The benefit of this approach is I get to use prosumer hardware but at reasonable cost (total < $350 AUD). For the AP's I have just setup via pairing on the mobile app and use the same SSID and passwords which allows for easy roaming.
I'm contemplating upgrading to an Ubiquiti dream machine pro to replace the ER-X for more ports and ability to have video recording & security cameras but really happy with current setup from a wifi performance and stability perspective.
Evolution of cetaceans started around 50 million years ago https://en.m.wikipedia.org/wiki/Evolution_of_cetaceans. You could probably make a solid argument that sharks being displaced as apex predators in the sea would have caused the die out of many species. Perhaps a crossover occurred around 19 million years ago.
I don't understand why people who care about security and have linux knowledge would use Synology/QNAP. They are both proprietary, often exposed to the internet, and packed full of so many features that they are consistently full of vulnerabilities (SynoLocker/QLocker etc).
I see the same with Spark vs Flink in that similarities outweigh differences. I wonder if this is some sort of emergent pattern in open source software.
1. Open source
2. 2GB ECC ram
3. 4 x SATA
4. Hackable GPIOs
Why do they keep marketing Debian as a different OS? It is literally just Linux and some GNU system components cobbled together.
The easiest way to do this is to use an ETL tool as hand coding this can be a bit hairy. You can use an open source etl tool like Talend for this as it has inbuilt Postgres or MySQL scd modules.