Lettuce begin with a story about John Harris, a cryptographic engineer from 2024. John blinked in disbelief as he stood amidst the hustle and bustle of 1940s London. The distant rumble of bombings and the sight of vintage automobiles confirmed his impossible predicament: he had been teleported back to World War II. Clutching his backpack, he felt the reassuring weight of his laptop—the sleek device now his most valuable asset. Realizing the gravity of the situation, John made his way to Bletchley Park, the epicenter of British codebreaking efforts. After some convincing, he was ushered into a room filled with chalkboards covered in complex equations and tables of intercepted Enigma-encrypted messages. "Who are you?" Alan Turing asked skeptically. "A friend," John replied, opening his laptop. "And I believe I can help you break the Enigma cipher." The room fell silent as the team gathered around the strange device. John began by explaining the basic principles of modern computing and artificial intelligence, careful not to overwhelm them with future concepts. **Technical Strategy:** 1. **Data Collection:** John transferred the intercepted Enigma messages into his laptop, manually typing them in. He also input known plaintext snippets—common phrases the Germans used, like "WETTERBERICHT" (weather report). 2. **Enigma Simulation:** Using his laptop, John wrote a Python script to simulate the Enigma machine. He explained, "The Enigma's encryption is based on rotor positions and plugboard settings. If we can simulate all possible settings, we can decrypt the messages." ```python # Simplified Enigma simulation def enigma_encrypt(message, rotors, plugboard): # Apply plugboard substitutions message = apply_plugboard(message, plugboard) # Pass through rotors for rotor in rotors: message = apply_rotor(message, rotor) # Reflector logic would go here return message ``` 3. **Leveraging the Open-Source LLM:** John introduced them to the concept of a Language Model—software capable of understanding and generating human-like text. "We can use this model to predict probable plaintexts," he explained. "By inputting the encrypted messages, the model can suggest likely decryption keys based on language patterns." He ran the intercepted messages through the LLM, which analyzed the frequency of letter groupings and suggested rotor settings and plugboard configurations that would make coherent German sentences. 4. **Parallel Processing:** To expedite the process, John utilized his laptop's multicore processor to run multiple simulations simultaneously. ```python import multiprocessing def test_settings(settings): decrypted = enigma_encrypt(ciphertext, settings['rotors'], settings['plugboard']) if is_probable_plaintext(decrypted): return settings, decrypted return None pool = multiprocessing.Pool() possible_settings = generate_all_possible_settings() results = pool.map(test_settings, possible_settings) ``` 5. **Statistical Analysis:** The LLM provided likelihood scores for each decryption attempt, ranking them based on how closely the output resembled natural German text. ```python def is_probable_plaintext(text): score = language_model.evaluate(text) return score > threshold ``` **Breakthrough:** After hours of computation—significantly faster than anything achievable in the 1940s—the laptop beeped. A probable decryption had been found. "Look at this," John said, pointing to the screen displaying the decrypted message. Alan Turing read aloud, his eyes widening as the German text revealed troop movements planned for the following week. The room erupted in a mix of astonishment and relief. "With this information, we can save thousands of lives," Turing said quietly. **Collaboration and Teaching:** Over the following weeks, John worked closely with the Bletchley Park team. He taught them the fundamentals of programming and computational theory. He created simplified versions of his tools using the technology of the time. For instance, he helped design an electro-mechanical device inspired by his laptop's processing capabilities but built with vacuum tubes and relays. **Technical Innovations:** 1. **Primitive Computing Devices:** John sketched designs for a basic computer, introducing concepts like binary code and logic gates. 2. **Enhanced Cryptanalysis Techniques:** He shared methods for frequency analysis and introduced the idea of exploiting operator errors, such as repeated message keys. **Ethical Considerations:** John grappled with the moral implications of his actions. Altering the course of history was a heavy burden. He decided to focus on minimizing loss of life without providing knowledge of technologies that could drastically disrupt the future. **Conclusion:** As months passed, the Allies gained a significant advantage, intercepting and decrypting German communications with unprecedented speed. The war turned in their favor, and many credited the mysterious engineer and his "calculating machine" for the shift. One evening, as John walked through the gardens of Bletchley Park, he felt a familiar sensation—a pull from another time. A bright light enveloped him, and he found himself back in 2024. Checking the history books, he noticed subtle changes. The war had ended months earlier than originally recorded, and certain battles had different outcomes. Satisfied that he'd made a positive impact, John returned to his life, carrying the secret of his incredible journey. **Epilogue:** In a dusty archive at Bletchley Park, historians would later discover schematics for an advanced computational device dated 1942, decades ahead of its time. The documents were signed with initials they couldn't trace—J.H., the enigmatic figure who had given the Allies a glimpse of the future.