# One Year of Observer > Published Dec 12 2025, last updated Dec 12 2025 > By Ryan Fleck and written without LLMs! > Original post at > An article of astonishing quality and insight. Happy Hacking! I have been running [Observer](https://observer.ryanfleck.ca) for over a year now. Given that it is the Christmas season - a time of relaxation, remembering our short time here and eternal things done for us, It is a good opportunity to look back and draw some lessons from a year of hacking on this thing. **Let's answer a few questions:** 1. How much data did I collect? 1. Was I able successfully extract tags from headlines? 1. What will I be moving forwards with? {{< toc >}} # Data Volume A simple check with `du` on my PostgreSQL folder reveals about 11GB of data consumed. ```bash du -hs #=> 11.2G ``` What about the consistency and quality of that data? Let's run a few queries to check within the Postgres database. ```sql select count(*) from keywords.terms; --> 2,023,694 select count(*) from media.items; --> 8,198,996 ``` ```sql SELECT schema_name, relname, pg_size_pretty(table_size) AS size, table_size FROM ( SELECT pg_catalog.pg_namespace.nspname AS schema_name, relname, pg_relation_size(pg_catalog.pg_class.oid) AS table_size FROM pg_catalog.pg_class JOIN pg_catalog.pg_namespace ON relnamespace = pg_catalog.pg_namespace.oid ) t WHERE schema_name NOT LIKE 'pg_%' ORDER BY table_size DESC; ``` ```org | schema_name | relname | size | table_size | |--------------------+------------------------------------------+------------+------------| | media | items | 5088 MB | 5335146496 | | media | idx_media_item_uri_source_unique | 703 MB | 737427456 | | keywords | terms_in_media_pkey | 616 MB | 645808128 | | media | idx_media_item_uri_institution_unique | 614 MB | 643923968 | | keywords | terms_in_media | 600 MB | 628809728 | | media | idx_media_item_uri | 598 MB | 626941952 | | keywords | daily_frequencies | 332 MB | 348340224 | | keywords | daily_frequencies_id_keyword_day_key | 263 MB | 275742720 | | media | idx_media_item_posted | 220 MB | 230334464 | | media | items_pkey | 209 MB | 219201536 | | media | idx_media_item_created | 181 MB | 189399040 | | keywords | daily_frequencies_pkey | 139 MB | 146104320 | | keywords | terms | 121 MB | 127082496 | | keywords | terms_label_term_key | 107 MB | 112164864 | | media | idx_media_item_source | 85 MB | 89604096 | | keywords | idx_daily_frequencies_day | 51 MB | 53673984 | | keywords | terms_pkey | 43 MB | 45465600 | | analysis | six_hour_summaries | 2232 kB | 2285568 | | media | sources | 208 kB | 212992 | | analysis | six_hour_summaries_pkey | 144 kB | 147456 | | media | institutions | 88 kB | 90112 | | media | idx_media_source_url | 56 kB | 57344 | | media | sources_pkey | 40 kB | 40960 | | media | idx_institution_urlkey | 32 kB | 32768 | | public | users_pkey | 16 kB | 16384 | | public | ux_users_email | 16 kB | 16384 | | public | schema_migrations_id_key | 16 kB | 16384 | | media | institutions_pkey | 16 kB | 16384 | | media | idx_media_text_analysis_v1_item | 8192 bytes | 8192 | | media | text_analysis_v1_pkey | 8192 bytes | 8192 | | media | keywords_id_seq | 8192 bytes | 8192 | | media | idx_through_keywords_to_text_analysis_v1 | 8192 bytes | 8192 | | media | keywords_pkey | 8192 bytes | 8192 | | media | sources_id_seq | 8192 bytes | 8192 | | media | institutions_id_seq | 8192 bytes | 8192 | | keywords | daily_frequencies_id_seq | 8192 bytes | 8192 | | keywords | terms_id_seq | 8192 bytes | 8192 | | media | items_id_seq | 8192 bytes | 8192 | | analysis | six_hour_summaries_id_seq | 8192 bytes | 8192 | | media | text_analysis_v1_id_seq | 8192 bytes | 8192 | ``` What about consistency? ```sql select count(id) as items, date_trunc('month', created) as month from media.items group by month order by month; ``` ```org | items | month | |--------+---------| | 305954 | 2024-09 | | 484560 | 2024-10 | | 533889 | 2024-11 | | 429309 | 2024-12 | | 474681 | 2025-01 | | 579925 | 2025-02 | | 653998 | 2025-03 | | 628588 | 2025-04 | | 646642 | 2025-05 | | 600735 | 2025-06 | | 614814 | 2025-07 | | 505834 | 2025-08 | | 533773 | 2025-09 | | 534749 | 2025-10 | | 476398 | 2025-11 | | 194795 | 2025-12 | ``` # Text Analysis Services [Stanford CoreNLP](https://stanfordnlp.github.io/CoreNLP/) and [SpaCY](https://spacy.io/) are both pre-LLM natural language processing tools. My pragmatic goal was to find a library or container to extract keywords and relationships from headlines to build a knowledge graph. These days, graph RAG utilizes many of the concepts I was moving towards to construct **semantic knowledge graphs** of the data they ingest. The service to extract keywords and triples also had to run on fairly ancient hardware - a 3rd gen dual-core Intel Core i3 processor. The hardware situation is now halfway remedied, and I have added a Mac Mini to my server closet to run basic low-cost inference, so this constraint is not as pressing as it was at the beginning of the year. **To summarize, my goals:** 1. I need a method to extract keywords and triples 2. It has to run in a container on old hardware I spun both up internally with independent compose files. ## CoreNLP ```yaml services: corenlp: image: nlpbox/corenlp restart: unless-stopped ports: # external:internal - "9209:9000" environment: JAVA_XMX: "4g" networks: - core-nlp-network networks: core-nlp-network: external: true ``` From here, I wrote a Clojure wrapper to pass strings and return data for **openie-triples** and **named entity recognition** procedures that are supported by CoreNLP: ```clojure (openie-triples "Novo Nordisk sheds its CEO, Jimbly Jumbles.") ;; Result: '({:subject "Novo Nordisk", :relation "sheds", :object "its CEO"} {:subject "its", :relation "CEO", :object "Jimbly Jumbles"} {:subject "Novo Nordisk", :relation "sheds", :object "Jimbly Jumbles"}) (ner "Novo Nordisk sheds its CEO, Jimbly Jumbles.") ;; Result: '({:ner "ORGANIZATION", :text "Novo Nordisk"} {:ner "TITLE", :text "CEO"} {:ner "PERSON", :text "Jimbly Jumbles"}) ``` Despite early success, I ended up abandoning CoreNLP long ago due to its inability to parse many headlines, which are not written in grammatically correct English. You may also notice some superfluous results above - I don't need to know that the CEO of "it's" is a guy named Jimbly. It is very tough to programmatically determine the value of these edges and nodes. A request to CoreNLP also took **4 seconds**, which without parallelism would just about fit the 20,000 article headlines per day into the ~86,400 seconds that make up one day. ```clojure (time ;; "Elapsed time: 4090.070472 msecs" (ner "News24 | Business Brief | Pepkor flags peppier profits; Novo Nordisk sheds its CEO. An overview of the biggest business developments in SA and beyond2.")) ;; Result: ({:tokenBegin 5, :docTokenEnd 6, :nerConfidences {:ORGANIZATION 0.92924116518148}, :tokenEnd 6, :ner "ORGANIZATION", :characterOffsetEnd 32, :characterOffsetBegin 26, :docTokenBegin 5, :text "Pepkor"} {:tokenBegin 10, :docTokenEnd 12, :nerConfidences {:ORGANIZATION 0.9979907226449}, :tokenEnd 12, :ner "ORGANIZATION", :characterOffsetEnd 68, :characterOffsetBegin 56, :docTokenBegin 10, :text "Novo Nordisk"} {:docTokenBegin 14, :docTokenEnd 15, :tokenBegin 14, :tokenEnd 15, :text "CEO", :characterOffsetBegin 79, :characterOffsetEnd 82, :ner "TITLE"}) ``` ## SpaCY ```yaml services: spacyapi: image: jgontrum/spacyapi:en_v2 ports: - "9210:80" restart: unless-stopped networks: - spacy-network networks: spacy-network: external: true ``` SpaCY is a classical machine-learning swiss army knife. > [SpaCY has] components for named entity recognition, part-of-speech > tagging, dependency parsing, sentence segmentation, text > classification, lemmatization, morphological analysis, entity > linking and more I ended up utilizing the entity-recognition, which could run 100 items through in 21 seconds. This was a much more acceptable speed than 4 seconds per item. Below are some output examples. ```clojure ;; Basic entity extraction (spacy-get-entities "BREAKING NEWS: Trump says he 'straightened out' rare earth-related issues with Xi.") ;; Result [{:end 20, :start 15, :text "Trump", :type "PERSON"} {:end 81, :start 79, :text "Xi", :type "PERSON"}] ;; Dependency extraction (spacy-get-dependencies "BREAKING NEWS: Trump says he 'straightened out' rare earth-related issues with Xi.") ;; Result: {:arcs [{:dir "left", :end 2, :label "nsubj", :start 1, :text "Trump"} {:dir "left", :end 4, :label "nsubj", :start 3, :text "he '"} {:dir "right", :end 4, :label "ccomp", :start 2, :text "straightened"} {:dir "right", :end 5, :label "prt", :start 4, :text "out'"} {:dir "right", :end 6, :label "dobj", :start 4, :text "rare earth-related issues"} {:dir "right", :end 7, :label "prep", :start 6, :text "with"} {:dir "right", :end 8, :label "pobj", :start 7, :text "Xi."}], :words [{:tag "NN", :text "BREAKING NEWS:"} {:tag "NNP", :text "Trump"} {:tag "VBZ", :text "says"} {:tag "PRP", :text "he '"} {:tag "VBD", :text "straightened"} {:tag "RP", :text "out'"} {:tag "NNS", :text "rare earth-related issues"} {:tag "IN", :text "with"} {:tag "NNP", :text "Xi."}]} ;; You'll need to store a dictionary with a guide for these tags! (spacy-get-entities "Early Bitcoin Miner Awakens $77 Million Fortune After 15-Year Slumber | A pioneering Bitcoin miner has stirred their dormant cryptocurrency holdings for the first time in 15 years. Arkham Intelligence reported this unprecedented activity, which has sent ripples through the crypto community. The miner’s patience has yielded an astounding $77 million windfall from their early involvement in Bitcoin. The miner acquired their Bitcoin through mining in") ;; Result: '[{:end 19, :start 6, :text "Bitcoin Miner", :type "PERSON"} {:end 39, :start 28, :text "$77 Million", :type "MONEY"} {:end 61, :start 54, :text "15-Year", :type "CARDINAL"} {:end 116, :start 109, :text "Bitcoin", :type "GPE"} {:end 186, :start 181, :text "first", :type "ORDINAL"} {:end 203, :start 195, :text "15 years", :type "DATE"} {:end 224, :start 205, :text "Arkham Intelligence", :type "ORG"} {:end 374, :start 349, :text "an astounding $77 million", :type "MONEY"} {:end 423, :start 416, :text "Bitcoin", :type "GPE"} {:end 457, :start 450, :text "Bitcoin", :type "GPE"}] ;; Some Key Categories { :PERSON "People, including fictional" :GPE "Countries, cities, states" :LOC "Non-GPE locations, mountain ranges, bodies of water" :ORG "Companies, agencies, institutions, etc." :MONEY "Monetary values, including unit" } ``` Though this is a huge improvement in performance, and the output is really great, but you'll notice some key problems with the data above. Bitcoin isn't a country/city/state, it's a money. When storing keywords, my initial implementation made room for different capitalization of terms, which results in a variety of different casings and labels for a single word. This presents a new and obvious problem to solve - how related are these terms? ```sql select * from keywords.terms where lower(term) = 'trump' order by id desc limit 20; ``` ``` id | label | term ---------+-------------+------- 1840200 | PERSON | TrUmP 1609960 | DATE | Trump 618151 | QUANTITY | Trump 347430 | ORG | TRUMP 340414 | PRODUCT | TRUMP 136251 | MONEY | Trump 94973 | WORK_OF_ART | TRUMP 88447 | FAC | Trump 68589 | EVENT | Trump 55607 | MONEY | TRUMP 45970 | PERSON | TRUMP 24686 | LANGUAGE | Trump 16995 | LAW | Trump 3592 | CARDINAL | Trump 2837 | ORG | Trump 1676 | GPE | Trump 532 | LOC | Trump 281 | WORK_OF_ART | Trump 144 | PRODUCT | Trump 71 | PERSON | Trump ``` Furthering this problem - groups of words are often misidentified as names, filling up the database with low quality data. Luckily the frequency of these detections is relatively low. ```sql select * from keywords.terms where lower(term) like '%trump%' and label = 'PERSON' -- There are many other keyword-labels with 'Trump' order by id desc limit 200; ``` ``` id | label | term ---------+--------+--------------------------------------------------------- 2023762 | PERSON | Trump Praises Indian- 2023226 | PERSON | Trump Congress 2023041 | PERSON | Sidestep Trump 2022539 | PERSON | Trump Attacks Clinton 2021844 | PERSON | Trump Memo 2021232 | PERSON | Trump Kennedy Center 2016537 | PERSON | TRUMP Trump 2014908 | PERSON | Donald Trump Reiterates 2014786 | PERSON | Donald Trump Reiterates ' 2014665 | PERSON | Trump Speech Thread 2013339 | PERSON | lets Trump 2010721 | PERSON | Rubio Calls Trump 2010571 | PERSON | Trump Orders Blockade 2010564 | PERSON | Nicolás Maduro Donald Trump 2008667 | PERSON | Trumps praise 2008360 | PERSON | Donald Trumpis 2008356 | PERSON | Susie Wiles Acknowledges Trump’s 2008138 | PERSON | Trump Expands The Toolkit 2007744 | PERSON | Trump Jr.’s 2007375 | PERSON | Trump Deflects Blame 2007048 | PERSON | Thinks Trump 2006874 | PERSON | Trump Sues BBC 2006685 | PERSON | Donald Trump Sues BBC 2005580 | PERSON | Trump Launches Political Attack on 2002889 | PERSON | Trump Pauses White House 2000309 | PERSON | Sue Trump Admin 2000268 | PERSON | Trump Says ' 2000254 | PERSON | Trump Prays 2000127 | PERSON | ‍Donald Trump 1999340 | PERSON | Trumpkin 1997712 | PERSON | Trump To Clinton 1997351 | PERSON | Will Trump ' 1996619 | PERSON | Warm Trump-Modi ...etc, etc, etc - this list goes on for a long time. ``` # Tagging Success Long story short - it's working alright, but not well. Trends are clearly visible when a global power decides to immolate a city, or a celebrity is shot, but many of the tags are superflous and not of note. ![](/observer/s14.png) ![](/observer/s15.png) I have not yet applied an algorithm to my frontend to remove tags that have not broken above or below a standard deviation (or any sort of anomaly detection algorithm) but would like to shortly. With current collection methods, here are the top terms that I am detecting and associating with incoming articles. Note that many of these terms, sometimes collected hundreds of thousands of times, have little to no meaning at all. Further data processing is required to distill the data captured in this layer into meaningful insights. ```sql select count(tim.id_item) as media_items, t.label, t.term from keywords.terms_in_media tim left join keywords.terms t on t.id = tim.id_keyword group by t.id order by media_items desc limit 100; ``` ``` count of related media_items | label | term --------------+----------+------------------------------------- 293,009 | ORDINAL | first 122,973 | GPE | US 86,930 | CARDINAL | two 77,687 | GPE | India 76,296 | CARDINAL | one 72,011 | ORG | The Nation Newspaper 63,800 | PERSON | Trump 63,150 | PERSON | Donald Trump 59,846 | DATE | 2025 59,155 | DATE | Monday 58,955 | DATE | Tuesday 57,544 | ORG | World News 57,519 | ORG | The Guardian Nigeria News - Nigeria 56,873 | DATE | Wednesday 55,109 | DATE | Thursday 52,443 | DATE | Friday 47,183 | GPE | Nigeria 46,565 | GPE | UK 46,012 | DATE | today 45,538 | CARDINAL | three 44,721 | GPE | Philippines 44,694 | GPE | U.S. 43,906 | DATE | Sunday 41,523 | DATE | Saturday 40,203 | GPE | China 39,396 | PRODUCT | Trump 37,111 | ORDINAL | second 35,166 | GPE | Russia 34,844 | GPE | Ukraine 34,443 | GPE | Israel 33,688 | NORP | Nigerian 33,221 | GPE | Australia 31,060 | GPE | Canada 27,563 | CARDINAL | four 27,160 | DATE | 2024 27,107 | NORP | Indian 25,625 | CARDINAL | 2 25,088 | NORP | Russian 23,706 | CARDINAL | five 23,292 | GPE | Gaza 22,981 | CARDINAL | Two 22,522 | CARDINAL | One 21,724 | ORG | MANILA 21,706 | GPE | Pakistan 21,155 | NORP | Israeli 20,939 | ORG | Premium Times Nigeria 20,251 | CARDINAL | 3 19,999 | NORP | Chinese 19,852 | CARDINAL | 10 19,794 | NORP | American 19,183 | GPE | Japan 18,668 | NORP | Australian 18,372 | GPE | London 18,274 | GPE | Iran 18,177 | NORP | British 17,975 | LOC | Trump 17,750 | ORG | Congress 17,510 | ORDINAL | third 17,167 | GPE | News24 16,998 | DATE | this week 16,883 | GPE | the United States 16,868 | DATE | this year 16,724 | ORG | Senate 16,293 | LOC | Europe 16,230 | ORG | NME 15,968 | CARDINAL | six 15,944 | GPE | Abuja 15,921 | GPE | South Africa 15,874 | CARDINAL | 5 15,522 | ORG | Tribune Online 15,439 | NORP | Canadian 15,351 | GPE | England 15,340 | CARDINAL | 1 14,934 | GPE | France 14,615 | NORP | Nigerians 14,354 | ORG | APC 14,311 | PERSON | Bola Tinubu 14,021 | GPE | Delhi 13,532 | GPE | Ireland 13,489 | NORP | French 13,442 | TIME | night 13,355 | DATE | 2023 13,254 | DATE | 2026 12,866 | ORG | EU 12,654 | DATE | Today 12,500 | ORG | Hamas 12,438 | GPE | Washington 12,407 | CARDINAL | 4 12,302 | CARDINAL | Three 12,172 | GPE | Sydney 12,158 | ORG | BJP 12,148 | CARDINAL | 2024 12,146 | NORP | Instagram 12,125 | DATE | last year 11,993 | GPE | Britain 11,941 | DATE | yesterday 11,910 | NORP | European 11,865 | TIME | morning 11,791 | DATE | last week 11,689 | ORG | SA (100 rows) ``` # Conclusion & Next Steps While I haven't spent nearly the amount of time I would have liked to on this side project, I have learned a lot reading about traditional NLP methods and discovering the strengths and weaknesses of different approaches. Unfortunately, LLMs are **excellent one-shot triple extractors**, and I imagine the amount of research pointed towards classical NLP and these types of methods will drop to nearly zero as a result. My next steps for this program is to migrate my database to a newer version of PostgreSQL with PostGIS and PGVector, in order to plot these news articles based on any locations mentioned in the headline and body, and to integrate a vector search to group similar headlines. **Most importantly** - I will begin utilizing my own self-hosted LLMs, breaking reliance on services hosted by big tech and eliminating my rate limits. This may come at the expense of speed, quality, and context window. Here's to another year - *happy hacking!* > Thank you for reading! > Find more content at > Source page: > Site index: [llms.txt](https://ryanfleck.ca/llms.txt)